JP2022153632A - Information processing device, information processing method, and information processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To appropriately extract similar content.

SOLUTION: An information processing device has an acquisition unit and a retrieval unit. The acquisition unit acquires graph information in which a plurality of nodes corresponding to each of a plurality of contents, which is an application performed in a predetermined device, are connected in accordance with similarity of the plurality of contents, and content information about one content. The retrieval unit extracts similar contents which are the contents similar to one content, of the plurality of contents, by retrieving the graph information acquired by the acquisition unit.

SELECTED DRAWING: Figure 3

COPYRIGHT: (C)2023,JPO&INPIT

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, techniques for extracting various information have been provided. For example, techniques are provided for estimating similarities between concepts in consideration of relative relationships between concepts belonging to other categories.

Japanese Patent Application Laid-Open No. 2007-213151

Masajiro Iwasaki, "Neighborhood Retrieval Using Approximate k-Nearest Neighbor Graph Using Tree Structured Index", Transactions of Information Processing Society of Japan, 2011/2, Vol. 52, No. 2. pp.817-828.

However, with the above-described conventional technology, it may be difficult to appropriately extract similar content. For example, it is difficult to extract desired information only by estimating similarities between concepts belonging to other categories. For example, it is not always possible to estimate the similarity of information belonging to the same category, and in such cases, it is difficult to extract desired information.

The present application has been made in view of the above, and an object thereof is to provide an information processing apparatus, an information processing method, and an information processing program for appropriately extracting similar content.

An information processing apparatus according to the present application provides graph information in which a plurality of nodes corresponding to each of a plurality of contents, which are applications executed on a predetermined device, are connected according to the similarity of the plurality of contents; an acquisition unit for acquiring content information about content; and a search for extracting similar content, which is content similar to the one content from among the plurality of contents, by searching the graph information acquired by the acquisition unit. and a part.

According to one aspect of the embodiment, it is possible to appropriately extract similar content.

FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. FIG. 2 is a diagram illustrating a configuration example of an information processing system according to the embodiment; FIG. 3 is a diagram illustrating a configuration example of an information processing apparatus according to the embodiment; 4 is a diagram illustrating an example of a content information storage unit according to the embodiment; FIG. FIG. 5 is a diagram illustrating an example of an index information storage unit according to the embodiment; 6 is a diagram illustrating an example of a graph information storage unit according to the embodiment; FIG. 7 is a diagram illustrating an example of a model information storage unit according to the embodiment; FIG. FIG. 8 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 9 is a flowchart illustrating an example of generation processing according to the embodiment. FIG. 10 is a diagram illustrating an example of feature amount extraction according to the embodiment. FIG. 11 is a flowchart illustrating an example of search processing using graph information. FIG. 12 is a diagram illustrating an example of content popularity determination. FIG. 13 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing apparatus.

Hereinafter, modes for implementing an information processing apparatus, an information processing method, and an information processing program (hereinafter referred to as "embodiments") according to the present application will be described in detail with reference to the drawings. The information processing apparatus, information processing method, and information processing program according to the present application are not limited to this embodiment. Also, in each of the following embodiments, the same parts are denoted by the same reference numerals, and overlapping descriptions are omitted.

(embodiment)
[1. information processing]
An example of information processing according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. In FIG. 1, an information processing apparatus 100 graph-structures content, which is an application (hereinafter simply referred to as an "application") executed in a predetermined device such as a terminal device 10 (see FIG. 2), as an example of content. A case of extracting content similar to one content (hereinafter also referred to as “similar content”) by searching graph data (graph information) is shown. Note that the predetermined device may be various computers such as a smart phone or a PC (Personal Computer). In addition, the content is not limited to an application, and may be various types of content such as video content such as movies, music content such as songs, and article content. In FIG. 1, the information processing apparatus 100 uses vector data (also referred to as “vector information” or simply “vector”) corresponding to each content to use graph information in which content is graph-structured. .

The information used by the information processing apparatus 100 is not limited to vectors, and may be information in any format as long as it is information that can express the similarity of each content. For example, the information processing apparatus 100 may use graph information in which content is graph-structured using predetermined data and values corresponding to each content. For example, the information processing apparatus 100 may use graph information in which content is graph-structured using a predetermined numerical value (for example, a binary value or a hexadecimal value) generated from each content. For example, instead of vectors, any form of data may be used as long as the distance (similarity) between data is defined. In the example of FIG. 1, it is determined whether or not one content is popular based on extracted similar content, and a service is provided for providing information on the determination result. may provide such services. Details on this point will be described later.

[1-1. About graph information]
The information processing apparatus 100 also performs information processing using graph data in which vectors (nodes) are connected by directed edges, as indicated by graph information GR11 in FIG. Graph information as indicated by graph information GR11 in FIG. It may be acquired from another external device such as the providing device 50 (see FIG. 2).

Also, the term "directed edge" used herein means an edge that allows data to be traced in only one direction. In the following description, the source traced by the edge, that is, the node that is the starting point, is the reference source, and the destination that is traced by the edge, that is, the node that is the end point, is the reference destination. For example, a directed edge connecting a predetermined node "A" to a predetermined node "B" indicates an edge having a reference source of node "A" and a reference destination of node "B". Edges connecting nodes are not limited to directed edges, and may be various edges. For example, the edge connecting each node may be a directionless edge connecting the nodes. For example, the edges connecting each node may be mutually referable edges. For example, the edges connecting each node may all be bidirectional edges.

For example, an edge having node "A" as a reference in this way is called an output edge of node "A". Further, for example, an edge having the node "B" as a reference in this way is referred to as an input edge of the node "B". In other words, the term "output edge" and "input edge" as used herein refers to the difference in which of the two nodes to which one directed edge connects is regarded as the center. are the output and input edges. In other words, an output edge and an input edge are relative concepts. For one directed edge, it is an output edge when the reference node is regarded as the center, and the reference node is regarded as the center. is an input edge. Note that, in the present embodiment, directed edges such as output edges and input edges are targeted for edges, and therefore directed edges may be simply referred to as "edges" below.

For example, the information processing apparatus 100 performs processing on nodes corresponding to a huge amount of content on the order of millions to hundreds of millions of units, but only some of them are shown in the drawing. In the example of FIG. 1, to simplify the explanation, eight nodes are illustrated to explain the outline of the processing. For example, the information processing apparatus 100 acquires graph information including a plurality of nodes (vectors) such as nodes N1, N2, N3, etc., as indicated by graph information GR11 in FIG. Also, in the example of FIG. 1, each node in the graph information GR11 is connected to a predetermined number of edges (output edges) from the node closest to the node. For example, the predetermined number may be various values such as 2, 5, 10, 100, etc., depending on the purpose and application. For example, if the predetermined number is 2, output edges from the node N1 are connected to the node closest to the node N1 and the two nodes closest to the node N1. Note that the distance as an index indicating similarity may be any distance as long as it can be applied as the distance between vectors (N-dimensional vectors). Various distances may be used.

Also, when "node N* (* is an arbitrary number)" is described in this way, it indicates that the node is identified by the node ID "N*". For example, when "node N1" is described, the node is identified by the node ID "N1".

In graph information GR11 in FIG. 1, node N10 is connected to edge E7, which is a directed edge toward node N7. That is, node N10 is connected to node N7 by edge E7. When "edge E* (* is an arbitrary numerical value)" is described in this way, it indicates that the edge is identified by the edge ID "E*". For example, when "edge E11" is described, the edge is identified by the edge ID "E11". For example, an edge E7 connecting the node N10 as a reference source and the node N7 as a reference destination enables tracing from the node N10 to the node N7. In this case, the directed edge E7 is an output edge when identified with the node N10 as the center, and an input edge when identified with the node N7 as the center. Also, the bidirectional arrows in the graph information GR11 of FIG. 1 indicate that directed edges from both nodes to the other node are connected. For example, a two-way arrow between node N2 and node N451 in graph information GR11 connects two edges: a directed edge from node N2 to node N451 and a directed edge from node N451 to node N2. indicates that

Further, the graph information GR11 in FIG. 1 may be Euclidean space. Graph information GR11 shown in FIG. 1 is a conceptual diagram for explaining distances between vectors, and the graph information GR11 is a multidimensional space. For example, the graph information GR11 shown in FIG. 1 is illustrated in a two-dimensional manner because it is illustrated on a plane, but it is assumed to be a multi-dimensional space such as 100-dimensional space or 1000-dimensional space.

Here, the distance between vector data indicates similarity of content, and the closer the distance, the more similar. In this embodiment, the distance of each node in the graph information GR11 is used as the degree of similarity between corresponding objects. For example, it is assumed that the similarity of content corresponding to each node is mapped as the distance between nodes in the graph information GR11. For example, it is assumed that the similarity between concepts corresponding to each node is mapped to the distance between each node. Here, in the example shown in FIG. 1, the similarity between objects with short distances between nodes in the graph information GR11 is high, and the similarity between objects with long distances between nodes in the graph information GR11 is low. For example, in the graph information GR11 in FIG. 1, the node identified by the node ID "N35" and the node identified by the node ID "N693" are close to each other, that is, the distance between them is short. Therefore, it indicates that the object corresponding to the node identified by the node ID "N35" and the object corresponding to the node identified by the node ID "N693" have a high degree of similarity.

Further, for example, in the graph information GR11 in FIG. 1, the node identified by the node ID "N7" and the node identified by the node ID "N2" are remote, that is, the distance is long. Therefore, the similarity between the object corresponding to the node identified by the node ID "N7" and the object corresponding to the node identified by the node ID "N2" is low.

[1-2. Vector generation example]
Each node (vector) here corresponds to each object (content). If the content information is numerical data indicating various states of content, it can be treated as it is as vector data. However, if the content information is text, images, or audio, conversion to vector data is required. Therefore, in the example of FIG. 1, the object may be a multidimensional (N-dimensional) vector generated from the feature amount extracted from the content information of each content.

For example, the information processing apparatus 100 may generate an N-dimensional vector from content information of each content using a model for extracting features of content information. In the example of FIG. 1, the information processing apparatus 100 uses the model (model M1) identified by the model ID "M1" as shown in the model information storage unit 124 (see FIG. 7) to store the content information of each content. Generate a vector from . As described above, when "model M* (* is an arbitrary numerical value)" is described, it indicates that the model is identified by the model ID "M*". For example, when "model M1" is described, the model is identified by the model ID "M1". Further, as shown in the model information storage unit 124, the model M1 is a model used for use "feature extraction (application)", that is, for feature extraction from content data (content information). It indicates that the data is "model data MDT1".

For example, the information processing apparatus 100 inputs content information of arbitrary content to the model M1, calculates the value of each element (neuron) in the model M1, and outputs information similar to the input content information. For example, the information processing apparatus 100 may extract the value of each element (neuron) in the intermediate layer as a feature amount and generate N-dimensional vector data corresponding to each content.

Here, an example of generation of vector data corresponding to each content is shown using FIG. FIG. 10 is a diagram illustrating an example of feature amount extraction according to the embodiment. FIG. 10 is a conceptual diagram of the model M1. Note that in FIG. 10, illustration of lines indicating respective connection relationships of respective elements (neurons) is omitted. As shown in FIG. 10, the model M1 includes an input layer IL, an intermediate layer CL, and an output layer OL. For example, the input layer IL of the model M1 is a layer into which content information is input. Also, the output layer OL is a layer that outputs information similar to the input content information in response to input to the input layer IL.

Also, for example, the most compressed layer RP in the central portion of the intermediate layer CL is a layer that expresses the features of the input content information. For example, in the intermediate layer CL of the model M1, the portion from the input layer IL to the compression layer RP corresponds to the portion where the encoding process is performed. In the intermediate layer CL of the model M1, the portion from the input layer IL to the compression layer RP corresponds to a portion that performs processing for compressing the features of the input content information. For example, in the intermediate layer CL of the model M1, the portion from the compression layer RP to the output layer OL corresponds to a portion that performs decoding processing. In the intermediate layer CL of the model M1, the portion from the compression layer RP to the output layer OL corresponds to a portion that performs processing for restoring compressed content information.

For example, the information processing apparatus 100 may use the information of the neuron NL1, the neuron NL2, etc. included in the compression layer RP as the vector. For example, when content information of a certain content is input, the information processing apparatus 100 uses the calculated value VE1 corresponding to the neuron NL1 and the calculated value VE2 corresponding to the neuron NL2 as vector elements (one-dimensional values). may be extracted. For example, when content information of a certain content is input, the information processing apparatus 100 may extract the calculated value VE1 corresponding to the neuron NL1 as the first-dimensional element of the content vector. Further, for example, when content information of a certain content is input, the information processing apparatus 100 may extract the calculated value VE2 corresponding to the neuron NL2 as the second-dimensional element of the content vector. In this way, the information processing apparatus 100 may generate a vector corresponding to each content by inputting content information of each content to the model M1. Note that the information processing apparatus 100 may acquire vectors corresponding to each content from another external apparatus such as the information providing apparatus 50 or the like. Note that the information processing apparatus 100 may use the value itself corresponding to each neuron, or may use a value obtained by multiplying the value corresponding to each neuron by a predetermined coefficient, as each element of the vector. In the example of FIG. 1, each element (value) of the vector is an integer to simplify the explanation. good too.

Note that the information processing apparatus 100 may use information of not only the elements (neurons) of the compression layer RP but also other elements (neurons) in the intermediate layer CL for the vectors. For example, the information processing apparatus 100 may use the information of the encoding part neuron NL3, the decoding part neuron NL4, or the like for the vector. For example, when content information of a certain content is input, the information processing apparatus 100 uses the calculated value VE3 corresponding to neuron NL3 and the calculated value VE4 corresponding to neuron NL4 as vector elements (one-dimensional values). may be extracted. Note that the above is just an example, and the information processing apparatus 100 may perform feature extraction from content information using various models, not limited to the autoencoder. For example, it is possible to generate a model trained to distinguish between content suitable for a given purpose such as startup members and content not suitable, and extract the intermediate layer as vector data. Alternatively, a model may be generated by a similarity learning method such as triplet loss. Also, the information processing apparatus 100 may perform feature extraction without using a model. For example, the information processing apparatus 100 may extract information corresponding to features (features) set by an administrator or the like of the information processing apparatus 100 from the content information and generate a vector. For example, the information processing apparatus 100 may extract information corresponding to the genre (category) of the content and the feature (feature) such as the content from the content information and generate a vector.

Further, for example, the information processing device 100 may acquire the model M1 from another external device such as the information providing device 50 . The information processing apparatus 100 may generate the model M1 by inputting the content information CDT1, CDT2, CDT451, etc. of each content stored in the content information storage unit 121 (see FIG. 4). For example, the information processing apparatus 100 may generate the model M1 by inputting the content information CDT1, CDT2, CDT451, etc. of each content. For example, the content information CDT1, CDT2, CDT451, etc. may include the genre (category) and content of the content. For example, when the content is an application, the content information such as the content information CDT1, CDT2, and CDT451 includes information indicating categories such as "weather", "route", and "game", image information indicating the startup screen, and so on. , and information indicating the billing amount. For example, when content is article content, content information such as content information CDT1, CDT2, and CDT451 may include information such as character information and image information corresponding to the article content. Also, for example, content information such as content information CDT1, CDT2, and CDT451 may include information such as image information and audio information corresponding to the moving image content when the content is moving image content. In addition, when the content is an application, the content information may be only the source code (character information).

The information processing apparatus 100 includes an input layer to which content information of content is input, an output layer, a first element belonging to any layer from the input layer to the output layer other than the output layer, and a first layer. and a second element whose value is calculated based on the weight of the element and the first element. By performing an operation based on the first element and the weight of the first element, the model M1 may be generated that outputs from the output layer the same information as the information input to the input layer. Note that the information processing apparatus 100 may acquire a model used for vector generation from another external apparatus such as the information providing apparatus 50 or the like.

[1-3. Processing example]
From here, a case where the information processing apparatus 100 acquires content information of one content from the user and provides a service based on content similar to the one content will be described as an example. In the example of FIG. 1, the terminal device 10 is used by a user U1 who belongs to a predetermined software development company. For example, the user U1 may be a user who develops applications at a company that develops applications for smartphones. The example of FIG. 1 shows a case where the user U1, who is the developer of the smartphone application, desires information on whether content X, which is an application developed by him/herself, will become popular.

First, the terminal device 10 transmits content information CDT11 of content X to the information processing device 100 . In the example of FIG. 1, the content information CDT11 and the like include information about the genre (category) and details of the content (application). Note that the content information CDT11 and the like may be only the source code (character information) of the content (application).

Then, the information processing apparatus 100 acquires content information regarding one content (step S11). In the example of FIG. 1, the information processing apparatus 100 acquires the content information CDT11 regarding the content X from the terminal device 10 of the user U1. The information processing apparatus 100 acquires content information CDT11 regarding the content X from the terminal device 10 (see FIG. 2) carried by the content X. FIG.

Then, the information processing apparatus 100 generates a vector used for searching graph information from content information corresponding to one content. In the example of FIG. 1, the information processing apparatus 100 generates a vector corresponding to the content X by the process indicated by the process group PS11. The information processing apparatus 100 inputs the content information CDT11 regarding the content X to the model M1 (step S12). Specifically, the information processing apparatus 100 inputs the content information CDT11 of the content X to the model M1. Then, the information processing apparatus 100 generates a vector using information in the model M1 after input of the content information CDT11 (step S13). For example, the information processing apparatus 100 generates vector data using each element in the model M1 to which the content information CDT11 is input.

In the example of FIG. 1, the information processing apparatus 100 generates vector data VD11 (also simply referred to as "vector VD11") using the values of the elements in the model M1 to which the content information CDT11 is input. For example, the information processing apparatus 100 calculates a value VE1 (see FIG. 10) corresponding to the neuron NL1 and a value VE2 (see FIG. 10) corresponding to the neuron NL2 of the model M1 when the content information CDT11 of the content X is input. to generate a vector. For example, when the content information CDT11 of the content X is input, the information processing apparatus 100 may extract the calculated value VE1 corresponding to the neuron NL1 as the first-dimensional element of the vector VD11. Further, for example, when content information of a certain content is input, the information processing apparatus 100 generates a vector VD11 by using the calculated value VE2 corresponding to the neuron NL2 as the second-dimensional element of the vector VD11. In the example of FIG. 1, the information processing apparatus 100 generates a vector VD11 whose first-dimensional element is "35" and whose second-dimensional element is "63."

Then, the information processing apparatus 100 searches for content similar to the content X (hereinafter also referred to as "similar content") (step S14). For example, the information processing apparatus 100 may search for content similar to the content X by appropriately using various conventional techniques such as the neighborhood search technique disclosed in Non-Patent Document 1. For example, the information processing apparatus 100 uses graph information GR11 in which nodes with high similarity are connected by edges.

In the example of FIG. 1, the information processing apparatus 100 searches for content similar to content X using graph information GR11 and index information IND11, as indicated by information group INF11. For example, the information processing apparatus 100 acquires graph information GR11 regarding content from the graph information storage unit 123 (see FIG. 6). Further, for example, the information processing apparatus 100 stores index information IND11 used for determining a node (hereinafter also referred to as "origin vector") serving as a starting point for searching in the graph information GR11 from the index information storage unit 122 (see FIG. 5). get. Note that the index information IND11 may be generated by the information processing device 100, or the information processing device 100 may acquire the index information IND11 from another external device such as the information providing device 50 or the like.

Then, the information processing apparatus 100 uses the index information IND11 to determine (specify) an origin vector corresponding to one content (query). In the example of FIG. 1, the information processing apparatus 100 uses the index information IND11 to determine (specify) the origin vector corresponding to the vector VD11 of the content X. FIG. That is, the information processing apparatus 100 uses the vector VD11 and the index information IND11 to determine the origin vector in the graph information GR11.

The index information IND11 in FIG. 1 has a hierarchical structure shown in the index information storage section 122 in FIG. For example, the index information IND11 indicates that the nodes (vectors) in the first layer located directly below the root RT are the nodes VT1, VT2, and the like. Also, for example, the index information IND11 indicates that the nodes of the second layer immediately below the node VT2 are the nodes VT2-1 to VT2-4 (not shown). Also, for example, the index information IND11 indicates that the nodes of the third layer immediately below the node VT2-2 are the node N35, the node N451, and the node N693, that is, nodes (vectors) in the graph information GR11.

For example, the information processing apparatus 100 determines the origin vector in the graph information GR11 using tree-structured index information as indicated by the index information IND11 in FIG. 1 (step S15). In the example of FIG. 1, after generating the vector VD11, the information processing apparatus 100 traces the index information IND11 from top to bottom to identify the origin vector that is a candidate for the vicinity of the index information IND11. An origin vector corresponding to a search query (a piece of content) can be determined.

For example, the information processing apparatus 100 may determine the origin vector corresponding to the vector VD11 by tracing the index information IND11 from the root RT to leaf nodes (nodes (vectors) in the graph information GR11). In the example of FIG. 1, for example, the information processing apparatus 100 determines the node N451 as the origin vector by tracing the index information IND11 from the root RT to the node N451. For example, the information processing apparatus 100 may determine the reached leaf node as the origin vector by tracing the index information IND11 from the root RT to the leaf node using various conventional techniques related to the tree structure. For example, the information processing apparatus 100 may determine the origin vector by tracing down the index information IND11 based on the degree of similarity with the vector VD11. For example, the information processing apparatus 100 may determine which of the nodes VT1, VT2, etc. is followed from the root RT based on the degree of similarity between the vector VD11 and the nodes VT1, VT2. For example, the information processing apparatus 100 may determine to trace from the root RT to the node VT2, which has the highest degree of similarity with the vector VD11, among the nodes VT1, VT2, and the like. Further, for example, the information processing apparatus 100 may determine to trace from the node VT2 to the node VT2-2 having the highest degree of similarity with the vector VD11 among the nodes VT2-1 to VT2-4. Further, for example, the information processing apparatus 100 may determine to trace from the node VT2-2 to the node N451, which has the highest degree of similarity with the vector VD11 among the nodes N35, N451, N693, and the like. Note that the example of FIG. 1 shows a case where one origin vector is determined for the sake of simplicity of explanation, but the information processing apparatus 100 may determine a plurality of origin vectors. For example, the information processing apparatus 100 may determine a plurality of vectors (nodes) such as nodes N451, N35, N693, and N2 as origin vectors. Instead of using the index information IND11, one or more nodes may be randomly selected from the graph information GR11 at the start of the search and used as the starting vector, or one or more nodes specified in advance may be used as the starting point. It can also be a vector.

Then, the information processing apparatus 100 extracts content similar to the content X by searching the graph information GR11 (step S16). For example, the information processing apparatus 100 extracts nodes located near the node N451 as similar content. For example, the information processing apparatus 100 extracts nodes that are close to the node N451 as similar content. For example, the information processing apparatus 100 extracts nodes reachable from the node N451 as similar content by tracing edges starting from the node N451. For example, the information processing apparatus 100 extracts a predetermined number (for example, 2 or 10) of nodes as similar content. For example, the information processing apparatus 100 may extract content similar to the content X by performing search processing as shown in FIG. 11, which will be described later in detail. In the example of FIG. 1, the information processing apparatus 100 extracts the node N451 and the node N35 as similar content by searching the graph information GR11 with the node N451 as the starting point.

Then, the information processing apparatus 100 acquires information regarding the index value of the similar content. In the example of FIG. 1, the information processing apparatus 100 acquires index values of similar content from the content information storage unit 121 (see FIG. 4). Here, the index value of the content may be various index values such as sales of the content and the number of times of user's contribution regarding the content, as long as it is information used for determining whether the content is popular. For example, when the content is an application, the number of downloads, the number of users, or the like may be used as the index value. Also, the information processing apparatus 100 may use a plurality of index values.

In the example of FIG. 1, sales of the content (application) are used as the index value of the content. In the example of FIG. 1, the information processing apparatus 100 acquires information such as the index value VL451 of the node N451 (content #451) and the index value VL35 of the node N35 (content #35) as shown in the similarity information SLT. For example, an index value VL451 indicates sales of content #451, and an index value VL35 indicates sales of content #35. For example, the index value VL451 and the index value VL35 are assumed to be specific numerical values such as 2,000,000 yen and 100,000,000 yen.

Then, the information processing apparatus 100 determines an index value (determination index value) used for determination (step S17). In the example of FIG. 1, the information processing apparatus 100 determines based on information such as the index value VL451 of the node N451 (content #451) and the index value VL35 of the node N35 (content #35) as shown in the similarity information SLT. determine the index value for

For example, the information processing apparatus 100 determines the average evaluation value (index value) of the similar content included in the similarity information SLT as the determination index value. In the example of FIG. 1, the information processing apparatus 100 determines the average of the index values of the similar content extracted in step S16 as the determination index value. That is, the information processing apparatus 100 determines the average of the index value VL451 of the content #45 and the index value VL35 of the content #35 included in the similarity information SLT as the determination index value. In this case, the information processing apparatus 100 adds the index value VL451 of the content #45 and the index value VL35 of the content #35 to calculate the total value of the index values of the similar content. Then, the information processing apparatus 100 divides the sum of the index values of the similar contents by the number of similar contents to calculate the index value for determination. For example, when there are two similar contents, namely, content #45 and content #35, the information processing apparatus 100 divides the total value obtained by adding the index value VL451 and the index value VL35 by the number of similar contents "2". Thus, the index value for determination is calculated. In the example of FIG. 1, the information processing apparatus 100 determines a determination index value DVL11 as shown in the determination information VLT.

Note that the above is an example, and the information processing apparatus 100 may determine any evaluation value (index value) as the determination index value based on the index value of the similar content included in the similarity information SLT. For example, the information processing apparatus 100 may determine the highest index value among the similarity information SLT as the determination index value. For example, the information processing apparatus 100 may determine the lowest index value among the similarity information SLT as the determination index value. For example, the information processing apparatus 100 may determine the index value of the content most similar to the content X among the similarity information SLT as the determination index value.

Then, the information processing apparatus 100 performs determination processing using the determination index value (step S18). For example, the information processing apparatus 100 determines whether the content X is popular by comparing the determination index value with a predetermined threshold value. For example, the information processing apparatus 100 determines that the content X is popular when the determination index value is equal to or greater than a predetermined threshold. For example, the information processing apparatus 100 determines that the content X is not popular when the determination index value is less than a predetermined threshold. In the example of FIG. 1, the information processing apparatus 100 performs determination processing using a threshold TH11 as indicated in the threshold information TLT. For example, the threshold TH11 is assumed to be a specific numerical value such as 5,000,000 yen or 100,000,000 yen. The information processing apparatus 100 may acquire the threshold TH11 from the storage unit 120 (see FIG. 3) storing the threshold TH11, or may acquire the threshold TH11 from an external information processing apparatus. For example, when the information processing apparatus 100 acquires the threshold TH11 from the storage unit 120 , the threshold TH11 is stored in the storage unit 120 . In the following description, it is assumed that the threshold TH11 is "10 million yen" and the determination index value DVL11 is "30 million yen".

As described above, the threshold TH11 is "10 million yen" and the determination index value DVL11 is "30 million yen". are compared, and it is determined that the determination index value DVL11 is equal to or greater than a predetermined threshold TH11. In this case, the information processing apparatus 100 determines that the content X is popular, as indicated by the popularity determination result DRS, because the determination index value DVL11 satisfies the criterion that the determination index value DVL11 is equal to or greater than the predetermined threshold TH11.

Then, the information processing apparatus 100 provides a service based on the determination result. In the example of FIG. 1, the information processing apparatus 100 provides the terminal device 10 with information (popularity determination information) indicating that the content X is likely to become popular (popular content) (step S19). For example, the information processing apparatus 100 transmits trend determination information indicating that the content X is trendy content to the terminal device 10 . Thereby, the user U1 can determine that the content X may become popular.

On the other hand, when determining that the determination index value DVL11 is less than the predetermined threshold TH11, the information processing apparatus 100 determines that the content X is not popular. Then, the information processing apparatus 100 transmits to the terminal device 10 popularity determination information indicating that there is a high possibility that the content X will not become popular. As a result, the user U1 can determine that there is a high possibility that the content X will not become popular, and can change the specifications of the content X to develop popular content.

As described above, the information processing apparatus 100 uses the graph information GR11 and the index information IND11 to extract content similar to the content X acquired from the user U1. For example, the information processing apparatus 100 uses the index information IND11 to determine the origin vector in the graph information GR11 when extracting content similar to the content X. FIG. Then, the information processing apparatus 100 extracts content similar to the content X by searching the graph information GR11 with the determined starting vector as a starting point. Accordingly, the information processing apparatus 100 can appropriately extract similar content.

For example, even if content similar to content X (similar content) does not seem to be related to content X at first glance, the information processing apparatus 100 uses the graph information to identify content similar to content X. can be extracted and similar content can be extracted appropriately. For example, there is a method of identifying content that is suitable for a given purpose, such as XX games or YY article content, using a technique such as deep learning, but there are a wide variety of factors that affect whether it is suitable for a given purpose. In this case, there is a problem that it is difficult to improve the accuracy of identification because there are few examples of each element. For example, there is a method of using a model generated by a learning method such as deep learning, inputting content information into the model, and identifying whether the content is suitable for a given purpose from the output score. When such elements exist, it is difficult to improve the accuracy of the model because there are few examples of each element, and it is difficult to improve the accuracy of identification. On the other hand, the information processing apparatus 100 performs a search using graph information in which a plurality of vectors are graph-structured and generated based on features extracted from each content. can be determined, and whether or not one content is included in a plurality of content can be appropriately determined. Therefore, the information processing apparatus 100 can appropriately extract similar content.

[1-4. About service]
In the example of FIG. 1, determination of whether content is popular has been described as an example, but the information processing apparatus 100 may provide various services using extracted similar content information. This point will be described below.

[1-4-1. Provision of information about users]
For example, the information processing apparatus 100 may provide information about the user who uses the content using the extracted similar content information. For example, the information processing apparatus 100 may use the extracted similar content information to provide information indicating what kind of users use one content.

In the example of FIG. 1 , the information processing apparatus 100 uses the information of the user who uses the content #451 and the content #35, which are similar contents, to provide the user with information indicating what kind of users will use the content X. It may be provided to U1. The information processing apparatus 100 uses the information of the user who uses the content #451 and the content #35, which are similar contents, to provide the user U1 with information (analysis information) indicating what kind of user uses the content X. may provide. For example, the information processing apparatus 100 may provide user U1 with attribute information of users who use content #451 and content #35. For example, when there are many male users in their twenties who use content #451 and content #35, the information processing apparatus 100 provides analysis information indicating that content X is likely to be used by male users in their twenties. You may transmit to the terminal device 10 of U1.

Further, for example, the information processing apparatus 100 may transmit analysis information indicating the ratio of each attribute among users who use the content #451 or the content #35 to the terminal device 10 of the user U1. For example, if 80% of the users who use the content #451 or the content #35 are men and 20% are women, the information processing apparatus 100 determines that 80% are men and 20% are women. You may transmit the analysis information which shows that to the terminal device 10 of the user U1.

[1-4-2. Determination of Generated Content]
In the example of FIG. 1, the information processing apparatus 100 determines the popularity of the content X acquired from the user U1, but the information processing apparatus 100 may generate content and perform the popularity determination of the generated content. For example, the information processing apparatus 100 generates content such as an application, video content, music content, article content, etc., and extracts content similar to the generated content using graph information, thereby determining popularity of the generated content. may This point will be described with reference to FIG. FIG. 12 is a diagram illustrating an example of content popularity determination. The example of FIG. 12 shows a case where the content is an application. Also, the graph information GR21 may be the graph information GR11 in FIG. Note that description of the same points as in FIG. 1 will be omitted as appropriate.

First, the information processing device 100 generates content (step S51). It should be noted that the generation of content here includes the case where the information processing apparatus 100 itself generates the content and the case where the information processing apparatus 100 requests another external device to generate the content. The information processing apparatus 100 may generate the application Y on its own device, or may request another external device to generate the application Y and acquire the application Y from the other external device that generated the application Y. . For example, the information processing apparatus 100 may generate the application Y at random using a technique related to automatic generation as appropriate. Further, for example, when the content is music content such as music, the information processing apparatus 100 extracts sections of several seconds or several bars from a plurality of past music and randomly combines them to generate music content. may For example, the information processing apparatus 100 may generate by randomly combining sections of a plurality of popular songs in the past. For example, the information processing apparatus 100 may generate by randomly combining a plurality of music sections that satisfy a trend-related condition, such as sales exceeding a predetermined threshold value (for example, 1 billion yen). Note that the information processing apparatus 100 is not limited to the above method, and may generate content using any method such as a technique related to automatic generation or random generation as long as content can be generated. For example, the information processing apparatus 100 may request the information providing apparatus 50 to generate the application Y, and acquire the application Y from the information providing apparatus 50 that generated the application Y. In the example of FIG. 12, the information processing apparatus 100 generates application Y. In the example of FIG.

Then, the information processing apparatus 100 determines the popularity of the content using the graph information (step S52). In the example of FIG. 12, the information processing apparatus 100 determines popularity of the application Y using the graph information GR21. For example, the information processing apparatus 100 extracts information on content (similar content) similar to the application Y from the graph information GR21 for one generated content (application Y). For example, the information processing apparatus 100 acquires content information about application Y (content information YLD). For example, the information processing apparatus 100 acquires the graph information GR21 from the graph information storage unit 123. FIG. For example, the information processing apparatus 100 acquires graph information GR21 in which a plurality of nodes corresponding to each content are connected according to the similarity of the plurality of contents. Then, the information processing apparatus 100 generates a vector (vector YVD) from the content information YDT of the application Y using the model M1 stored in the model information storage unit 124 .

Then, the information processing apparatus 100 determines the origin vector to be the node YNE (corresponding to the application AP5 in FIG. 12) using the generated vector YVD and the index information. Then, the information processing apparatus 100 extracts similar content (similar group) of the application Y by searching the graph information GR21 with the node YNE, which is the starting vector, as the starting point. For example, the information processing apparatus 100 extracts a predetermined number (for example, two) of nodes as similar content by searching the graph information GR21 starting from the node YNE. In the example of FIG. 12, the information processing apparatus 100 extracts the application AP1 (node NET1) and the application AP5 (node NET5) included in the similar content range SAR11 as similar content.

Then, the information processing apparatus 100 determines whether or not the application Y is popular using the index value of the extracted similar content. For example, the information processing apparatus 100 acquires information about index values of similar content. In the example of FIG. 12, the number of downloads of the content (application) is used as the index value of the content. In the example of FIG. 12, the information processing apparatus 100 acquires the index value of similar content from the content information storage unit 121 (see FIG. 4). Then, the information processing apparatus 100 determines an index value used for determination (determination index value). In the example of FIG. 12 , the information processing apparatus 100 sets the index value VLAP1 of the application (for example, the application AP1) with the largest number of downloads among the application AP1 and the application AP5 to the determination index value (hereinafter referred to as the “determination index value DVL51”). The index value VLAP1 is assumed to be a specific numerical value such as 50,000 times or 1,000,000 times.

Then, the information processing apparatus 100 performs determination processing using the determination index value. For example, the information processing apparatus 100 determines whether the content X is popular by comparing the determination index value with a predetermined threshold value. In the example of FIG. 12, the information processing apparatus 100 performs determination processing using the threshold TH51. For example, the threshold TH11 is assumed to be a specific numerical value such as 250,000 times or 5,000,000 times. The information processing apparatus 100 may acquire the threshold TH51 from the storage unit 120 (see FIG. 3) storing the threshold TH51, or may acquire the threshold TH51 from an external information processing apparatus.

For example, when the information processing apparatus 100 determines that the determination index value DVL51 is equal to or greater than a predetermined threshold TH51, the information processing apparatus 100 determines that the content X is popular. On the other hand, when determining that the determination index value DVL11 is less than the predetermined threshold TH11, the information processing apparatus 100 determines that the content X is not popular. In this way, the information processing apparatus 100 can appropriately generate popular content by determining the popularity of the generated content. Further, the information processing apparatus 100 may decide to sell or release the generated content when determining that the generated content will become popular. The information processing device 100 may then provide the generated content to various external devices such as the terminal device 10 and the information providing device 50 .

[1-5. Index information]
The index information (index data) shown in the example of FIG. 1 is an example, and the information processing apparatus 100 may search graph information using various types of index information. Further, for example, the information processing apparatus 100 may generate index data used for searching. For example, the information processing apparatus 100 generates a search index for searching high-dimensional vectors as index data. The high-dimensional vector referred to here may be, for example, a vector with several hundred to several thousand dimensions, or a vector with more dimensions.

For example, the information processing apparatus 100 may generate a search index related to a tree structure (tree structure) as shown in FIG. 1 as index data. For example, the information processing apparatus 100 may generate a search index for a kd-tree (k-dimensional tree) as index data. For example, the information processing apparatus 100 may generate a search index for a VP tree (Vantage-Point tree) as index data.

Further, for example, the information processing apparatus 100 may generate index data having another tree structure. For example, the information processing apparatus 100 may generate various types of index data in which leaves of tree-structured index data are connected to graph data. For example, the information processing apparatus 100 may generate various types of index data in which leaves of the tree-structured index data correspond to nodes in the graph data. When performing a search using such index data, the information processing apparatus 100 may search for graph data from a leaf (node) reached by tracing the index data.

Note that the index data as described above is merely an example, and the information processing apparatus 100 may generate index data having any data structure as long as the query in the graph data can be identified at high speed. . For example, the information processing apparatus 100 generates index data by appropriately using various conventional techniques such as a technique related to binary space division, as long as it is possible to identify a node in graph information corresponding to a query at high speed. may For example, the information processing apparatus 100 may generate index data having any data structure as long as the index is compatible with high-dimensional vector searches. For example, the information processing apparatus 100 may use information about a graph-type search index as described in Non-Patent Document 1 as index information. The information processing apparatus 100 can use the index data and the graph data as described above to enable a more efficient search for predetermined content.

[2. Configuration of information processing system]
As shown in FIG. 2 , the information processing system 1 includes a terminal device 10 , an information providing device 50 and an information processing device 100 . The terminal device 10, the information providing device 50, and the information processing device 100 are connected via a predetermined network N so as to be communicable by wire or wirelessly. FIG. 2 is a diagram illustrating a configuration example of an information processing system according to the embodiment; The information processing system 1 shown in FIG. 2 may include multiple terminal devices 10 , multiple information providing devices 50 , and multiple information processing devices 100 .

The terminal device 10 is an information processing device used by a user. The terminal device 10 accepts various operations by the user. In addition, below, the terminal device 10 may be described as a user. That is, hereinafter, the user can also be read as the terminal device 10 . The terminal device 10 described above is implemented by, for example, a smart phone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. For example, the terminal device 10 may be an information processing device in which a predetermined server system is used by an administrator. For example, the terminal device 10 collects content information and transmits the content information to the information processing device 100 .

The information processing apparatus 100 is an information processing apparatus that determines whether or not one of a plurality of contents is included in a plurality of contents by retrieving graph information using an origin vector as a starting point. For example, the information processing apparatus 100 acquires content information related to one content, and determines an origin vector based on the content information and information related to an origin vector that is a starting point for searching graph information.

The information processing device 100 is an information processing device that stores information for providing various information to users and the like. For example, when the information processing device 100 acquires content information (hereinafter also referred to as “query information” or “query”) of one content from the terminal device 10, the information processing device 100 searches for content (vector information etc.) similar to the query, A search result is provided to the terminal device 10 . In the example of FIG. 1, the information processing apparatus 100 acquires content information of one content from the terminal device 10, searches for content similar to the one content, and provides the search result to the terminal device 10 as similar content. . Further, for example, the data provided by the information processing device 100 to the terminal device 10 may be the name of the content or the content information of the content itself, or a URL (Uniform Resource Locator) or other data for referring to corresponding data. It may be information.

The information providing device 50 is an information processing device that stores information for providing various information to the information processing device 100 . For example, the information providing device 50 may store content information and the like collected from various external devices such as web servers. For example, the information providing device 50 is an information processing device that provides various information such as graph information, index information, and models to the information processing device 100 .

[3. Configuration of Information Processing Device]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described using FIG. FIG. 3 is a diagram illustrating a configuration example of an information processing apparatus according to the embodiment; As shown in FIG. 3, the information processing apparatus 100 has a communication section 110, a storage section 120, and a control section . The information processing apparatus 100 includes an input unit (for example, a keyboard, a mouse, etc.) that receives various operations from an administrator of the information processing apparatus 100, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. may have.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. The communication unit 110 is connected to a network (for example, network N in FIG. 2) by wire or wirelessly, and transmits and receives information to and from the terminal device 10 and the information providing device 50 .

(storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 according to the embodiment has a content information storage unit 121, an index information storage unit 122, a graph information storage unit 123, and a model information storage unit 124, as shown in FIG. The storage unit 120 may also store information such as the threshold TH11.

(Content information storage unit 121)
The content information storage unit 121 according to the embodiment stores various types of information regarding content (object). For example, the content information storage unit 121 stores content IDs and vector data. In the example of FIG. 4, content information about content is stored. 4 is a diagram illustrating an example of a content information storage unit according to the embodiment; FIG. The content information storage unit 121 shown in FIG. 4 includes items such as "content ID", "content", "content information", "index value", and "vector information".

“Content ID” indicates identification information for identifying content (object). "Content" indicates the specific name, content, etc. of the content identified by the content ID. In the example of FIG. 4, the content is indicated by an abstract code such as "content #1". good. "Index value" indicates a predetermined index value of the content identified by the content ID. For example, if the index is "sales", the "index value" may be a value indicating sales of the content. Further, for example, when the content is an application, the "index value" may be the number of downloads, the number of users, or the like. Note that the “index value” is not limited to the above, and may be any index value as long as it relates to content. Further, when using a plurality of index values, the number of items of "index value" may correspond to the number of index values to be used (a plurality of items). In the example of FIG. 4, the index value is indicated by an abstract code such as "VL1", but a specific numerical value corresponding to the index value, such as 1000 or 1,000,000, may be stored.

"Content information" indicates information about the content identified by the content ID. In the example of FIG. 4, the content information is indicated by an abstract code such as "CDT1", but each piece of content information contains information on various contents such as the content genre (category) and content identified by the content ID. may contain. “Vector information” indicates vector information corresponding to the content (object) identified by the content ID. That is, in the example of FIG. 4, the vector data (vector information) corresponding to the object is registered in association with the content ID that identifies the content (object).

For example, in the example of FIG. 4, the content (object) identified by the content ID “CT1” is associated with multidimensional (N-dimensional) vector information of “10, 24, 54, 2 . indicates For example, for the content #1, multidimensional (N-dimensional) vector information of "10, 24, 54, 2..." indicating the feature of the content #1 is extracted from the content information CDT1 by the model M1 or the like. indicates that Also, the example of FIG. 4 indicates that the index value of content #1 is "VL1".

Note that the content information storage unit 121 may store various types of information, not limited to the above, depending on the purpose.

(Index information storage unit 122)
The index information storage unit 122 according to the embodiment stores various information regarding indexes. FIG. 5 is a diagram illustrating an example of an index information storage unit according to the embodiment; Specifically, in the example of FIG. 5, the index information storage unit 122 indicates tree-structured index information. In the example of FIG. 5, the index information storage unit 122 includes items such as "root layer", "first layer", "second layer", and "third layer". It should be noted that not only "first layer" to "third layer" but "fourth layer", "fifth layer", "sixth layer", etc. may be included according to the number of layers of the index.

The “root hierarchy” indicates the root (top) hierarchy that is the starting point for determining the origin node using the index. "First layer" stores information for identifying (specifying) a node (node or vector in graph information) belonging to the first layer of the index. A node stored in the "first layer" is a node corresponding to a layer directly connected to the root of the index.

"Second layer" stores information that identifies (specifies) a node (node or vector in graph information) belonging to the second layer of the index. A node stored in the "second layer" is a node corresponding to the immediately lower layer connected to a node in the first layer. "Third layer" stores information for identifying (specifying) a node (node or vector in graph information) belonging to the third layer of the index. A node stored in the "third layer" is a node corresponding to the immediately lower layer linked to a node in the second layer.

For example, in the example shown in FIG. 5, the index information storage unit 122 stores information corresponding to the index information IND11 in FIG. For example, the index information storage unit 122 indicates that the nodes of the first layer are the nodes VT1 to VT3. Numerical values in parentheses under each node indicate vector values corresponding to each node.

Also, for example, the index information storage unit 122 indicates that the nodes of the second layer immediately below the node VT2 are the nodes VT2-1 to VT2-4. Also, for example, the index information storage unit 122 indicates that the nodes in the third layer immediately below the node VT2-2 are the nodes (vectors) in the graph information GR11 of the nodes N35, N451, and N693.

It should be noted that the index information storage unit 122 may store various types of information, not limited to the above, depending on the purpose.

(Graph information storage unit 123)
The graph information storage unit 123 according to the embodiment stores various types of information regarding graph information. 6 is a diagram illustrating an example of a graph information storage unit according to the embodiment; FIG. In the example of FIG. 6, the graph information storage unit 123 has items such as "node ID", "content ID", and "edge information". "Edge information" includes information such as "edge ID" and "reference destination".

"Node ID" indicates identification information for identifying each node (content) in graph data. "Content ID" indicates identification information for identifying the content (object).

"Edge information" indicates information about edges connected to the corresponding node. In the example of FIG. 6, "edge information" indicates the case where the edge is a directed edge, and indicates information regarding the output edge output from the corresponding node. "Edge ID" indicates identification information for identifying an edge connecting nodes. "Reference destination" indicates information indicating a reference destination (node) connected by an edge. That is, in the example of FIG. 6, for a node ID that identifies a node, a reference destination ( node) are associated and registered.

For example, the example of FIG. 6 indicates that the node (vector) identified by the node ID "N1" corresponds to the content (object) identified by the content ID "CT1". It also indicates that the edge identified by the edge ID "E11" is connected to the node (vector) identified by the node ID "N25" from the node identified by the node ID "N1". That is, the example of FIG. 6 indicates that the node (vector) identified by the node ID "N1" can be traced to the node (vector) identified by the node ID "N25".

It should be noted that the graph information storage unit 123 may store various types of information, not limited to the above, depending on the purpose. For example, the graph information storage unit 123 may store lengths of edges connecting nodes (vectors). That is, the graph information storage unit 123 may store information indicating distances between nodes (vectors).

(Model information storage unit 124)
The model information storage unit 124 according to the embodiment stores information about models. For example, the model information storage unit 124 stores model information (model data) generated by the generation process. 7 is a diagram illustrating an example of a model information storage unit according to the embodiment; FIG. The model information storage unit 124 shown in FIG. 7 includes items such as "model ID", "usage", and "model data". Although only the models M1 and M2 are illustrated in FIG. 7, a large number of model information such as M21 and M22 may be stored according to each application (prediction target).

"Model ID" indicates identification information for identifying a model. For example, the model identified by model ID "M1" corresponds to model M1 shown in the example of FIG. "Use" indicates the use of the corresponding model. Also, "model data" indicates the data of the associated corresponding model. For example, "model data" includes information including nodes in each layer, functions adopted by each node, connection relations of nodes, and connection coefficients set for connections between nodes.

For example, in the example shown in FIG. 7, the model (model M1) identified by the model ID "M1" is used for "feature extraction (application)" and is used to extract features from input content information. indicates that It also indicates that the model data of the model M1 is the model data MDT1.

The model M1 (model data MDT1) includes an input layer to which content information of content is input, an output layer, and a first element belonging to any layer from the input layer to the output layer other than the output layer. , and a second element whose value is calculated based on the weight of the first element and the first element. It is a model for causing a computer to function so that the same information as that input to the input layer is output from the output layer by performing calculations based on the first element and the weight of the first element as elements.

It is also assumed that the models M1, M2, etc. are implemented by a neural network having one or more intermediate layers, such as a DNN (Deep Neural Network). In this case, for example, the first element included in the models M1 and M2 corresponds to any node of the input layer or the intermediate layer. Also, the second element corresponds to the next node, which is a node to which the value is transmitted from the node corresponding to the first element. Also, the weight of the first element corresponds to the connection coefficient, which is the weight considered for the value transmitted from the node corresponding to the first element to the node corresponding to the second element.

Here, it is assumed that the models M21, M22, etc. are realized by a regression model represented by "y=a1*x1+a2*x2+...+ai*xi". In this case, for example, the first elements included in the models M21 and M22 correspond to input data (xi) such as x1 and x2. Also, the weight of the first element corresponds to the coefficient ai corresponding to xi. Here, the regression model can be viewed as a simple perceptron with an input layer and an output layer. When each model is regarded as a simple perceptron, the first element can be regarded as a node of the input layer, and the second element can be regarded as a node of the output layer.

It should be noted that the model information storage unit 124 may store various types of model information, not limited to the above, depending on the purpose.

(control unit 130)
Returning to the description of FIG. 3, the control unit 130 is a controller, and is stored in a storage device inside the information processing apparatus 100 by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). Various programs (corresponding to an example of an information processing program) are executed by using the RAM as a work area. Also, the control unit 130 is a controller, and is implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 130 performs information processing according to the models M1, M2, etc. stored in the model information storage unit 124 to create an input layer to which content information of content is input, an output layer, and layers from the input layer to the output layer. A first element belonging to any layer other than the output layer and a second element whose value is calculated based on the first element and the weight of the first element, and is input to the input layer Information similar to the information input to the input layer is obtained by performing an operation based on the first element and the weight of the first element, with each element belonging to each layer other than the output layer as the first element. is output from the output layer.

As shown in FIG. 3, the control unit 130 includes an acquisition unit 131, a generation unit 132, a determination unit 133, a search unit 134, and a provision unit 135, and has information processing functions and actions described below. realize or perform Note that the internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 3, and may be another configuration as long as it performs information processing described later.

(Acquisition unit 131)
Acquisition unit 131 acquires various types of information. For example, the acquisition unit 131 acquires various information from the storage unit 120 . For example, the acquisition unit 131 acquires various information from the content information storage unit 121, the index information storage unit 122, the graph information storage unit 123, the model information storage unit 124, and the like. The acquisition unit 131 also acquires various types of information from an external information processing device. The acquisition unit 131 acquires various types of information from the terminal device 10 and the information providing device 50 .

The acquisition unit 131 acquires graph information in which a plurality of nodes corresponding to each of a plurality of contents are connected according to the similarity of the plurality of contents, and content information related to one content. The acquisition unit 131 acquires graph information in which a plurality of vectors corresponding to each of a plurality of contents are connected according to the similarity of the plurality of contents, and content information related to one content. Acquisition unit 131 acquires graph information in which a plurality of vectors representing features of each of a plurality of contents are connected according to similarity.

The acquiring unit 131 acquires graph information in which a plurality of vectors whose elements are feature amounts extracted from each of a plurality of contents using a predetermined model are connected according to similarity. By inputting information about a plurality of contents into a predetermined model, the acquisition unit 131 obtains graph information in which a plurality of vectors whose elements are feature amounts of each of the extracted contents are connected according to similarity. to get

The acquisition unit 131 acquires trend information about the popularity of similar content. The acquisition unit 131 acquires popularity information indicating an index value relating to popularity of similar content. The acquisition unit 131 acquires content information regarding one content from the terminal device used by the user.

For example, the acquisition unit 131 acquires a plurality of nodes (vectors) that serve as data search content. For example, the acquiring unit 131 acquires a directed edge group including a plurality of nodes and a plurality of directed edges connecting each of the plurality of nodes.

For example, the acquisition unit 131 acquires graph information (graph data) from an external information processing device. For example, the acquisition unit 131 acquires graph information from the graph information storage unit 123 . For example, the acquisition unit 131 acquires graph information. In the example of FIG. 1, the acquisition unit 131 acquires graph information GR11.

For example, the acquisition unit 131 acquires index information (index data) from an external information processing device. For example, the acquisition unit 131 acquires index information from the index information storage unit 122 . For example, the acquisition unit 131 acquires tree-structured index information. In the example of FIG. 1, the acquisition unit 131 acquires index information IND11.

In addition, the acquisition unit 131 acquires content information regarding one content from the terminal device 10 used by the user. For example, the acquisition unit 131 acquires content information of one content as a search query. For example, the acquisition unit 131 acquires a search query regarding content. The acquisition unit 131 acquires the content information CDT11 regarding the content X from the terminal device 10 used by the user U1.

For example, the acquisition unit 131 acquires content information related to one content. For example, the acquisition unit 131 acquires the content information CDT11 regarding the content X from the terminal device 10 . For example, the acquisition unit 131 acquires the graph information GR11 regarding the content from the graph information storage unit 123. FIG. Further, for example, the acquiring unit 131 acquires index information IND11 used for determining a node (originating vector) serving as a starting point for searching in the graph information GR11 from the index information storage unit 122 (see FIG. 5).

In the example of FIG. 1, the acquisition unit 131 acquires index values of similar content from the content information storage unit 121 (see FIG. 4). The acquisition unit 131 acquires information such as the index value VL451 of the node N451 (content #451) and the index value VL35 of the node N35 (content #35) as shown in the similarity information SLT. Further, the acquiring unit 131 acquires the threshold TH11 from the storage unit 120 storing the threshold TH11.

(Generating unit 132)
The generator 132 generates various types of information. For example, the generation unit 132 generates a model shown in the model information storage unit 124 using learning data (content information) stored in the content information storage unit 121 . For example, the generation unit 132 generates a model (autoencoder) that outputs information similar to the input content information based on the learning data acquired by the acquisition unit 131 . For example, the generation unit 132 generates a model (autoencoder) that outputs information similar to the input content information, using the input content information itself as correct information.

For example, the generation unit 132 generates the model M1 and the like, and stores the generated model M1 and the like in the model information storage unit 124 . Note that the generation unit 132 may generate the model M1 using any learning algorithm. For example, the generator 132 generates the model M1 using a learning algorithm such as a neural network. As an example, when the generation unit 132 generates the model M1 or the like using a neural network, the model M1 or the like includes an input layer including one or more neurons, an intermediate layer including one or more neurons, and one or more neurons. and an output layer containing

The generation unit 132 generates an input layer to which content information of a content is input, an output layer, a first element belonging to any layer from the input layer to the output layer other than the output layer, and a first element and a second element whose value is calculated based on the weight of the first element. By performing calculations based on the one element and the weight of the first element, a model is generated in which the same information as that input to the input layer is output from the output layer.

For example, the generator 132 generates a model based on learning data. For example, the generation unit 132 generates a model by performing learning using the content information CDT1, CDT2, etc. in the content information storage unit 121 as learning data (teaching data).

For example, when the content information CDT1 is input, the generation unit 132 performs learning processing so that the model M1 outputs information similar to the content information CDT1. For example, when the content information CDT2 is input, the generation unit 132 performs learning processing so that the model M1 outputs information similar to the content information CDT2.

Note that the generation unit 132 is not limited to the model M1 as an autoencoder, and may generate a model M21 corresponding to content (entrance), a model M22 corresponding to content (entry into a building), and the like using various learning algorithms. . For example, the generation unit 132 generates the models M21, M22, etc. using learning algorithms such as neural networks, support vector machines (SVM), clustering, and reinforcement learning. As an example, when the generation unit 132 generates the models M21, M22, etc. using a neural network, the models M21, M22, etc. include an input layer including one or more neurons, an intermediate layer including one or more neurons, and a and an output layer containing the above neurons. Note that the information processing apparatus 100 may not have the generation unit 132 when acquiring a model from another external apparatus such as the information providing apparatus 50 .

In the example of FIG. 1, the generation unit 132 generates an N-dimensional vector from content information of each content using a model for extracting features of content information. The generating unit 132 generates a vector from the content information of each content using the model M1 shown in the model information storage unit 124 (FIG. 7).

Further, the generation unit 132 generates a vector used for searching graph information from content information corresponding to one content. In the example of FIG. 1, the generation unit 132 generates a vector corresponding to the content X by performing the processing shown in the processing group PS11. The generation unit 132 inputs the content information CDT11 regarding the content X to the model M1. Specifically, the generation unit 132 inputs content information CDT11 including information such as the genre (category) and details of the content X to the model M1. Then, the generation unit 132 generates a vector using the information in the model M1 after the input of the content information CDT11. For example, the generator 132 generates vector data using each element in the model M1 to which the content information CDT11 is input.

In the example of FIG. 1, the generator 132 generates the vector VD11 using the values of the elements in the model M1 to which the content information CDT11 is input. For example, the generation unit 132 uses the value VE1 (see FIG. 10) corresponding to the neuron NL1 and the value VE2 (see FIG. 10) corresponding to the neuron NL2 of the model M1 when the content information CDT11 of the content X is input. to generate a vector. For example, when the content information CDT11 of the content X is input, the generation unit 132 may extract the calculated value VE1 corresponding to the neuron NL1 as the first-dimensional element of the vector VD11. Further, for example, when the content information of the content is input, the generating unit 132 generates the vector VD11 by using the calculated value VE2 corresponding to the neuron NL2 as the second-dimensional element of the vector VD11. In the example of FIG. 1, the generator 132 generates a vector VD11 whose first-dimensional element is "35" and whose second-dimensional element is "63."

(Determination unit 133)
The determination unit 133 determines various types of information. The determination unit 133 determines the origin node based on the index information used for determining the origin node. For example, the determination unit 133 determines the origin vector based on the content information acquired by the acquisition unit 131 and the information on the origin vector that is the origin of the graph information search. For example, the determination unit 133 determines the starting vector based on the content information and information that serves as a reference for determining the starting vector for searching graph information. Further, the determination unit 133 determines the origin vector based on the index information used for determining the origin vector. Further, the determination unit 133 determines the origin vector based on the tree-structured index information.

In the example of FIG. 1, the determination unit 133 uses the index information IND11 to determine (specify) the origin vector corresponding to one content (query). For example, the determination unit 133 uses the index information IND11 to determine (specify) the origin vector corresponding to the vector VD11 of the content X. FIG. The determining unit 133 determines the starting vector in the graph information GR11 using the vector VD11 and the index information IND11.

For example, the determining unit 133 determines the origin vector in the graph information GR11 using tree-structured index information as indicated by the index information IND11 in FIG. In the example of FIG. 1, after generating the vector VD11, the determination unit 133 traces the index information IND11 from top to bottom to identify the origin vector that is a candidate for the neighborhood of the index information IND11, thereby efficiently searching. Determine the origin vector corresponding to the query (a piece of content).

For example, the determining unit 133 determines the origin vector corresponding to the vector VD11 by tracing the index information IND11 from the root RT to leaf nodes (nodes (vectors) in the graph information GR11). In the example of FIG. 1, the determining unit 133 determines the node N451 as the origin vector by tracing the index information IND11 from the root RT to the node N451.

The determination unit 133 determines an index value (determination index value) used for determination. In the example of FIG. 1 , the determination unit 133 determines the similarity information SLT based on information such as the index value VL451 of the node N451 (content #451) and the index value VL35 of the node N35 (content #35). Determine the index value.

In the example of FIG. 1, the determination unit 133 determines the average of the index values of the similar content included in the similarity information SLT as the determination index value. The determination unit 133 determines the average of the index values of the similar content as the determination index value. The determination unit 133 determines the average of the index value VL451 of the content #45 and the index value VL35 of the content #35 included in the similarity information SLT as the determination index value. In this case, the determination unit 133 adds the index value VL451 of the content #45 and the index value VL35 of the content #35 to calculate the total value of the index values of the similar content. Then, the determination unit 133 divides the sum of the index values of the similar contents by the number of similar contents to calculate the index value for determination. For example, when there are two similar contents, namely, the content #45 and the content #35, the determination unit 133 divides the total value obtained by adding the index value VL451 and the index value VL35 by the number of similar contents "2". Then, the index value for judgment is calculated. In the example of FIG. 1, the determination unit 133 determines the determination index value DVL11 as shown in the determination information VLT.

The determination unit 133 may determine any index value as the determination index value based on the index value of the similar content included in the similarity information SLT. For example, the determination unit 133 may determine the highest index value among the similarity information SLT as the determination index value. For example, the determination unit 133 may determine the lowest index value among the similarity information SLT as the determination index value. For example, the determination unit 133 may determine the index value of the content most similar to the content X among the similarity information SLT as the determination index value.

(Search unit 134)
The search unit 134 searches for various types of information. For example, the search unit 134 searches various information using the information acquired by the acquisition unit 131 . For example, the search unit 134 extracts various information. For example, the search unit 134 uses information acquired by the acquisition unit 131 to extract various types of information. For example, the search unit 134 performs various determination processes. For example, the search unit 134 uses information acquired by the acquisition unit 131 to determine various types of information. For example, the search unit 134 searches various information stored in the storage unit 120 . For example, the acquisition unit 131 searches various information stored in the content information storage unit 121, the index information storage unit 122, the graph information storage unit 123, the model information storage unit 124, and the like. For example, the search unit 134 performs calculation processing using various information. For example, the search unit 134 uses information about vectors to calculate the distance between the vectors. For example, the search unit 134 may use information on the length (distance) of edges connecting nodes (vectors) stored in the graph information storage unit 123, or may use vector information on each node ( It is also possible to calculate information on the length (distance) of the edge connecting the vectors) and use the information on the calculated length (distance).

The retrieving unit 134 retrieves graph information starting from a starting node, which is determined based on a predetermined criterion, among the plurality of nodes of the graph information acquired by the acquiring unit 131. extracts similar content, which is content similar to one content, from among a plurality of contents. The search unit 134 extracts similar content starting from the starting node determined by the determining unit 133 .

The search unit 134 determines whether one content is popular based on the trend information acquired by the acquisition unit 131 . When the index value of similar content satisfies a predetermined criterion, the search unit 134 determines that one content is popular content that may become popular. The search unit 134 compares the index value of similar content with a predetermined threshold value, and determines that one piece of content is popular content when the index value of similar content is equal to or greater than the predetermined threshold value.

The retrieving unit 134 retrieves the graph information from among the plurality of vectors of the graph information acquired by the acquiring unit 131, starting from the starting vector that is the starting point for searching the graph information determined based on a predetermined criterion. , to extract similar content that is content similar to one content from among a plurality of content. The search unit 134 extracts various information. For example, the search unit 134 extracts similar content, which is content similar to one content, from among a plurality of pieces of content by searching for graph information using the starting point vector determined by the determination unit 133 as a starting point.

The retrieving unit 134 retrieves graph information starting from a starting node, which is determined based on a predetermined criterion, among the plurality of nodes of the graph information acquired by the acquiring unit 131. determines whether one piece of content is included in a plurality of pieces of content. The search unit 134 determines whether one piece of content is included in a plurality of pieces of content, starting from the starting point node determined by the determination unit 133 .

In the example of FIG. 1, the search unit 134 searches for content similar to the content X. FIG. For example, the search unit 134 extracts content similar to the content X by searching the graph information GR11. The search unit 134 extracts nodes located near the node N451 as similar content. For example, the search unit 134 extracts nodes that are close to the node N451 as similar content. For example, the search unit 134 extracts nodes reachable from the node N451 as similar content by tracing edges starting from the node N451. For example, the search unit 134 extracts a predetermined number (for example, 2 or 10) of nodes as similar content. For example, the search unit 134 extracts content similar to the content X through search processing as shown in FIG. In the example of FIG. 1, the search unit 134 extracts the node N451 and the node N35 as similar content by searching the graph information GR11 with the node N451 as a starting point.

In the example of FIG. 1, the search unit 134 performs determination processing using the determination index value. The search unit 134 performs determination processing using the determination index value determined by the determination unit 133 . For example, the search unit 134 determines whether the content X is popular by comparing the determination index value with a predetermined threshold value. For example, the search unit 134 determines that the content X is popular when the determination index value is equal to or greater than a predetermined threshold. For example, if the determination index value is less than a predetermined threshold, the search unit 134 determines that the content X is not popular.

In the example of FIG. 1, the search unit 134 compares the determination index value DVL11 with a predetermined threshold TH11, and determines that the determination index value DVL11 is equal to or greater than the predetermined threshold TH11. In this case, the search unit 134 determines that the content X is popular, as shown in the popularity determination result DRS, because the determination index value DVL11 satisfies the criterion that the determination index value DVL11 is equal to or greater than the predetermined threshold TH11. For example, when the search unit 134 determines that the determination index value DVL11 is less than the predetermined threshold TH11, it determines that the content X is not popular.

(Providing unit 135)
The providing unit 135 provides various information. For example, the providing unit 135 transmits various information to the terminal device 10 and the information providing device 50 . For example, the providing unit 135 distributes various types of information to the terminal device 10 and the information providing device 50 . For example, the providing unit 135 provides various information to the terminal device 10 and the information providing device 50 . The providing unit 135 provides a predetermined service based on similar content extracted by the searching unit 134 . In addition, the providing unit 135 provides an information providing service regarding similar content. The providing unit 135 provides information on similar content to the terminal device 10 .

The providing unit 135 provides a predetermined service based on similar content extracted by the searching unit 134 . The providing unit 135 provides the terminal device 10 with a predetermined service. The providing unit 135 provides an information providing service regarding similar content.

For example, the providing unit 135 provides object IDs corresponding to queries as search results. For example, the providing unit 135 provides the information providing device 50 with the object ID selected by the searching unit 134 . The providing unit 135 provides the information providing device 50 with the object ID selected by the searching unit 134 as information indicating the vector corresponding to the query. Further, the providing unit 135 may provide the model generated by the generating unit 132 to an external information processing device.

For example, the providing unit 135 provides services based on the determination result. In the example of FIG. 1 , the providing unit 135 provides the terminal device 10 with information (popularity determination information) indicating that the content X is likely to become popular (popular content). For example, the providing unit 135 transmits popularity determination information indicating that the content X is popular content to the terminal device 10 . For example, the providing unit 135 transmits to the terminal device 10 popularity determination information indicating that the content X is unlikely to become popular.

[4. Information processing flow]
Next, the procedure of information processing by the information processing system 1 according to the embodiment will be described with reference to FIG. FIG. 8 is a flowchart illustrating an example of information processing according to the embodiment.

As shown in FIG. 8, the information processing apparatus 100 acquires content information regarding one content (step S101). In the example of FIG. 1 , the information processing apparatus 100 acquires content information CDT11 regarding content X from the terminal device 10 .

The information processing apparatus 100 acquires graph information regarding a plurality of contents (step S102). For example, the information processing apparatus 100 acquires graph information GR11 regarding content from the graph information storage unit 123 .

Then, the information processing apparatus 100 uses the model to generate a vector from the content information regarding one content (step S103). In the example of FIG. 1, the information processing apparatus 100 uses the model M1 stored in the model information storage unit 124 to generate the vector VD11 from the content information CDT11.

Then, the information processing apparatus 100 determines the origin vector using the generated vector and the index information (step S104). In the example of FIG. 1, the information processing apparatus 100 uses the vector VD11 and the index information IND11 stored in the index information storage unit 122 to determine the origin vector to be the node N451.

Then, the information processing apparatus 100 extracts similar content similar to the one content by searching the graph information (step S105). For example, the information processing apparatus 100 extracts content similar to one content as similar content by searching graph information.

The information processing apparatus 100 then provides a service based on the similar content (step S106). In the example of FIG. 1, the information processing apparatus 100 provides an information providing service indicating whether or not the content X is popular based on the information of the content #451 and the content #35 extracted as similar content.

[5. Generation processing flow]
Next, a procedure of generation processing by the information processing system 1 according to the embodiment will be described with reference to FIG. 9 . FIG. 9 is a flowchart illustrating an example of generation processing according to the embodiment.

As shown in FIG. 9, the information processing apparatus 100 acquires learning data (step S201). For example, the information processing device 100 acquires learning data from the content information storage unit 121 . For example, the information processing apparatus 100 acquires content information CDT1, CDT2, etc. from the content information storage unit 121 as learning data.

After that, the information processing apparatus 100 generates a model based on the learning data (step S202). For example, the information processing apparatus 100 uses learning data from the content information storage unit 121 to generate the model M1. For example, the information processing apparatus 100 generates the model M1 so that information (content information) similar to information (content information) input to the input layer is output from the output layer. For example, the information processing apparatus 100 generates a model M1 as an autoencoder that receives content information of content.

[6. Search example]
Here, an example of retrieval using the graph information described above will be shown. The search using graph information (graph data) is not limited to the following, and may be performed by various procedures. This point will be described with reference to FIG. 11 as an example. FIG. 11 is a flowchart illustrating an example of search processing using graph data (graph information). The search processing described below is performed by the search unit 134 of the information processing apparatus 100, for example. Also, the object referred to below may be read as a vector or a node. In the following description, the information processing apparatus 100 performs the search process, but the search process may be performed by another apparatus. For example, the information processing apparatus 100 uses vector data generated from content information of one content as a search query. For example, the information processing apparatus 100 searches for graph data using an origin vector determined based on index information and vector data generated from content information of one content. In the example of FIG. 1, the information processing apparatus 100 searches for the graph information GR11 starting from the node N451, which is the starting vector determined based on the vector VD11 of the content X and the index information IND11.

Here, the neighborhood object set N(G, y) is the set of neighborhood objects associated by the edge attached to node y. "G" may be predetermined graph data (for example, graph information GR11, etc.). For example, the information processing apparatus 100 executes k-nearest neighbor search processing.

For example, the information processing apparatus 100 sets the radius r of the hypersphere to ∞ (infinity) (step S300), and extracts the subset S from the existing object set (step S301). For example, the information processing apparatus 100 may extract, as the subset S, an object (node) selected as a root node (origin vector). In the example of FIG. 1, the information processing apparatus 100 may extract the node N451, which is the origin vector, etc., as the subset S. In the example of FIG. Also, for example, a hypersphere is a virtual sphere that indicates a search range. The objects included in the object set S extracted in step S301 are also included in the initial set of the object set R of the search results.

Next, the information processing apparatus 100 extracts an object having the shortest distance from the object y, where y is the search query object, among the objects included in the object set S, and sets it as an object s (step S302). In the example of FIG. 1, the information processing apparatus 100 extracts the object with the shortest distance from the vector VD11, which is the search query object, from among the objects included in the object set S, and sets it as the object s. For example, the information processing apparatus 100 extracts the node N451 having the shortest distance from the vector VD11 among the objects included in the object set S, and sets it as the object s. For example, when only the object (node) selected as the root node (originating vector) is an element of the object set S, the information processing apparatus 100 extracts the root node (originating vector) as the object s. . Next, the information processing apparatus 100 excludes the object s from the object set S (step S303).

Next, the information processing apparatus 100 determines whether or not the distance d(s, y) between the object s and the object y exceeds (exceeds) r(1+ε) (step S304). Here, ε is an expansion factor, and r(1+ε) is a value that indicates the radius of the search range (only nodes within this range are searched; making it larger than the search range increases the accuracy). be. When the distance d(s, y) between the object s and the object y exceeds r(1+ε) (step S304: Yes), the information processing device 100 outputs the object set R as a neighborhood object set of the object y (step S305), the process ends.

If the distance d(s, y) between the object s and the search query object y does not exceed r(1+ε) (step S304: No), the information processing apparatus 100 calculates the neighborhood object set N(G, s) of the object s. One object not included in the object set C is selected from the objects that are elements of , and the selected object u is stored in the object set C (step S306). The object set C is provided for convenience in order to avoid duplicate searches, and is set to an empty set at the start of processing.

Next, the information processing apparatus 100 determines whether or not the distance d(u, y) between the object u and the object y is r(1+ε) or less (step S307). If the distance d(u, y) between the object u and the object y is less than or equal to r(1+ε) (step S307: Yes), the information processing apparatus 100 adds the object u to the object set S (step S308). If the distance d(u, y) between the object u and the object y is not less than r(1+ε) (step S307: No), the information processing apparatus 100 performs determination (processing) in step S309.

Next, the information processing apparatus 100 determines whether or not the distance d(u, y) between the object u and the object y is less than or equal to r (step S309). When the distance d(u, y) between the object u and the object y exceeds r (step S309: No), the information processing apparatus 100 performs determination (processing) in step S315. If the distance d(u, y) between the object u and the object y is not equal to or less than r (step S309: No), the information processing apparatus 100 performs determination (processing) in step S315.

If the distance d(u, y) between the object u and the object y is less than or equal to r (step S309: Yes), the information processing apparatus 100 adds the object u to the object set R (step S310). Then, the information processing apparatus 100 determines whether or not the number of objects included in the object set R exceeds ks (step S311). The predetermined number ks is an arbitrarily determined natural number. For example, various settings such as ks=2 and ks=10 may be used. When the number of objects included in the object set R does not exceed ks (step S311: No), the information processing apparatus 100 performs determination (processing) in step S313.

If the number of objects included in the object set R exceeds ks (step S311: Yes), the information processing apparatus 100 selects the object with the longest (farthest) distance from the object y among the objects included in the object set R, It is excluded from the object set R (step S312).

Next, the information processing apparatus 100 determines whether or not the number of objects included in the object set R matches ks (step S313). If the number of objects included in the object set R does not match ks (step S313: No), the information processing apparatus 100 performs determination (processing) in step S315. If the number of objects included in the object set R matches ks (step S313: Yes), the information processing apparatus 100 determines that the object y has the longest (farthest) distance among the objects included in the object set R. The distance between the object and object y is set to a new r (step S314).

Then, the information processing apparatus 100 determines whether or not all objects have been selected from objects that are elements of the neighborhood object set N(G, s) of the object s and stored in the object set C (step S315). . If all the objects that are elements of the neighborhood object set N(G, s) of the object s have not been selected and stored in the object set C (step S315: No), the information processing apparatus 100 proceeds to step S306. and repeat the process.

When all objects are selected from the objects that are elements of the neighborhood object set N(G, s) of the object s and stored in the object set C (step S315: Yes), the information processing apparatus 100 returns the object set S is an empty set (step S316). If the object set S is not an empty set (step S316: No), the information processing apparatus 100 returns to step S302 and repeats the process. If the object set S is an empty set (step S316: Yes), the information processing apparatus 100 outputs the object set R and ends the process (step S317). For example, the information processing apparatus 100 may provide objects (nodes) included in the object set R as search results corresponding to the search query (input object y) to the terminal apparatus 10 or the like that performed the search. In the example of FIG. 1, the information processing apparatus 100 provides the node N451 and the node N35 included in the object set R as search results corresponding to the search query (the vector VD11 of the content X) to the terminal device 10 or the like that performed the search. You may For example, the information processing apparatus 100 provides content #451 corresponding to node N451 and content #35 corresponding to node N35 as content similar to content X to the terminal device 10 used by user U1.

[7. effect〕
As described above, the information processing apparatus 100 according to the embodiment has the acquisition unit 131 and the search unit 134 . The acquisition unit 131 acquires graph information in which a plurality of nodes corresponding to each of a plurality of contents are connected according to the similarity of the plurality of contents, and content information related to one content. The retrieving unit 134 retrieves graph information starting from a starting node, which is determined based on a predetermined criterion, among the plurality of nodes of the graph information acquired by the acquiring unit 131. extracts similar content, which is content similar to one content, from among a plurality of contents.

As described above, the information processing apparatus 100 according to the embodiment can appropriately extract similar content, which is content similar to one content from among a plurality of content, by searching graph information using the starting vector as a starting point. can be done.

Further, the information processing apparatus 100 according to the embodiment has a determination unit 133 . The determination unit 133 determines the origin node based on the index information used for determining the origin node. The search unit 134 extracts similar content starting from the starting node determined by the determining unit 133 .

Thus, the information processing apparatus 100 according to the embodiment can appropriately extract similar content by determining the origin vector based on the index information used for determining the origin vector.

Further, in the information processing apparatus 100 according to the embodiment, the determining unit 133 determines the origin vector based on the tree-structured index information.

In this way, the information processing apparatus 100 according to the embodiment can appropriately extract similar content by determining the origin vector based on the tree-structured index information.

Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires graph information in which a plurality of vectors representing features of each of a plurality of contents are connected according to similarity.

As described above, the information processing apparatus 100 according to the embodiment appropriately extracts similar content by acquiring graph information in which a plurality of vectors indicating the characteristics of each of a plurality of contents are connected according to similarity. can do.

Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 connects a plurality of vectors whose elements are feature amounts extracted from each of a plurality of contents using a predetermined model according to similarity. get graph information.

As described above, the information processing apparatus 100 according to the embodiment provides graph information in which a plurality of vectors whose elements are feature amounts extracted from each of a plurality of contents using a predetermined model are connected according to similarity. can appropriately extract similar content.

Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 inputs information about a plurality of contents into a predetermined model, and obtains a plurality of vectors whose elements are feature amounts of each of the extracted contents. obtains graph information connected according to similarity.

As described above, the information processing apparatus 100 according to the embodiment inputs information about a plurality of contents into a predetermined model, so that a plurality of vectors whose elements are the feature amounts of each of the extracted contents are similar to each other. Similar content can be appropriately extracted by acquiring graph information linked according to gender.

In addition, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires trend information about the popularity of similar content. The search unit 134 determines whether one content is popular based on the trend information acquired by the acquisition unit 131 .

As described above, the information processing apparatus 100 according to the embodiment appropriately determines whether one content is popular by determining whether one content is popular based on the popularity information about the popularity of similar content. can judge.

Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires popularity information indicating an index value relating to popularity of similar content. When the index value of similar content satisfies a predetermined criterion, the search unit 134 determines that one content is popular content that may become popular.

As described above, the information processing apparatus 100 according to the embodiment determines that one piece of content is popular content that may become popular when the index value of the similar content satisfies a predetermined criterion. It is possible to appropriately determine whether is prevalent.

In addition, in the information processing apparatus 100 according to the embodiment, the search unit 134 compares the index value of similar content with a predetermined threshold value, and if the index value of similar content is equal to or greater than the predetermined threshold value, one content item becomes popular. Determined as content.

As described above, the information processing apparatus 100 according to the embodiment compares the index value of similar content with a predetermined threshold value, and if the index value of similar content is equal to or greater than the predetermined threshold value, the content is identified as popular content. By determining that, it is possible to appropriately determine whether or not one content is popular.

The information processing apparatus 100 according to the embodiment also has a providing unit 135 . The providing unit 135 provides a predetermined service based on similar content extracted by the searching unit 134 .

In this way, the information processing apparatus 100 according to the embodiment can appropriately provide a service using information on similar content by providing a predetermined service based on the extracted similar content.

In addition, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires content information regarding one content from the terminal device used by the user. The providing unit 135 provides the terminal device 10 with a predetermined service.

As described above, the information processing apparatus 100 according to the embodiment acquires content information related to one content from the terminal device 10 used by the user, and provides a predetermined service to the terminal device 10 to obtain information related to similar content. can appropriately provide services using

Further, in the information processing apparatus 100 according to the embodiment, the providing unit 135 provides an information providing service regarding similar content.

In this way, the information processing apparatus 100 according to the embodiment can appropriately provide a service using information on similar content by providing an information providing service on similar content.

[8. Hardware configuration]
The information processing apparatus 100 according to the embodiments described above is implemented by a computer 1000 configured as shown in FIG. 13, for example. FIG. 13 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing apparatus. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, a HDD (Hard Disk Drive) 1400, a communication interface (I/F) 1500, an input/output interface (I/F) 1600, and a media interface (I/F). ) 1700.

The CPU 1100 operates based on programs stored in the ROM 1300 or HDD 1400 and controls each section. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. Communication interface 1500 receives data from other devices via network N, sends the data to CPU 1100, and transmits data generated by CPU 1100 to other devices via network N. FIG.

The CPU 1100 controls output devices such as displays and printers, and input devices such as keyboards and mice, through an input/output interface 1600 . CPU 1100 acquires data from an input device via input/output interface 1600 . CPU 1100 also outputs the generated data to an output device via input/output interface 1600 .

Media interface 1700 reads programs or data stored in recording medium 1800 and provides them to CPU 1100 via RAM 1200 . CPU 1100 loads such a program from recording medium 1800 onto RAM 1200 via media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. etc.

For example, when the computer 1000 functions as the information processing apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 executes a program or data (for example, model M1 (model data MDT1)) loaded on the RAM 1200 to It implements the functions of the control unit 130 . CPU 1100 of computer 1000 reads these programs or data (for example, model M1 (model data MDT1)) from recording medium 1800 and executes them. can be obtained.

As described above, some of the embodiments of the present application have been described in detail based on the drawings, but these are merely examples, and various modifications, It is possible to carry out the invention in other forms with modifications.

[9. others〕
Further, among the processes described in the above embodiments, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being performed manually can be performed manually. All or part of this can also be done automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

Also, each component of each device illustrated is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

Further, each process described in each embodiment described above can be appropriately combined within a range in which the contents of the process are not inconsistent.

Also, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

1 information processing system 100 information processing device 121 content information storage unit 122 index information storage unit 123 graph information storage unit 124 model information storage unit 130 control unit 131 acquisition unit 132 generation unit 133 determination unit 134 search unit 135 provision unit 10 terminal device 50 Information providing device N network

Claims (16)

Acquisition for acquiring graph information in which a plurality of nodes corresponding to each of a plurality of contents, which are applications executed on a predetermined device, are linked according to the similarity of the plurality of contents, and content information related to one content Department and
a search unit for extracting similar content, which is content similar to the one content from among the plurality of contents, by searching the graph information acquired by the acquisition unit;
An information processing device comprising: The search unit is
The similar content is extracted by searching the graph information starting from a starting point node determined based on a predetermined criterion among the plurality of nodes of the graph information and serving as a starting point for searching the graph information. The information processing apparatus according to claim 1, characterized by: a determination unit that determines the origin node based on index information used to determine the origin node;
further comprising
The search unit is
The information processing apparatus according to claim 2, wherein the similar content is extracted starting from the starting node determined by the determining unit. The decision unit
4. The information processing apparatus according to claim 3, wherein the starting node is determined based on the tree-structured index information. The acquisition unit
5. The method according to any one of claims 1 to 4, wherein graph information in which the plurality of nodes are connected is acquired according to the similarity of the plurality of vectors corresponding to each of the plurality of contents. Information processing equipment. The acquisition unit
6. The information processing apparatus according to claim 5, wherein the graph information is acquired in which the plurality of vectors indicating the features of the plurality of contents are connected according to similarity. The acquisition unit
6. Obtaining said graph information in which said plurality of vectors, whose elements are feature amounts extracted from each of said plurality of contents using a predetermined model, are connected according to similarity. 7. The information processing apparatus according to claim 6. The acquisition unit
By inputting the information about the plurality of contents into a predetermined model, the plurality of vectors whose elements are feature amounts of each of the extracted contents are converted into the graph information connected according to the similarity. 8. The information processing apparatus according to any one of claims 5 to 7, characterized in that: The acquisition unit
Acquiring fashion information about the popularity of similar content;
The search unit is
9. The information processing apparatus according to claim 8, wherein it is determined whether the one content is popular based on the trend information acquired by the acquisition unit. The acquisition unit
obtaining the trend information indicating an index value related to the trend of the similar content;
The search unit is
10. The information processing apparatus according to claim 9, wherein when the index value of the similar content satisfies a predetermined criterion, the one content is determined to be popular content that may become popular. The search unit is
comparing the index value of the similar content with a predetermined threshold, and determining that the one content is the popular content when the index value of the similar content is equal to or greater than the predetermined threshold; 11. The information processing apparatus according to claim 10. a provision unit that provides a predetermined service based on the similar content extracted by the search unit;
The information processing apparatus according to any one of claims 1 to 11, further comprising: The acquisition unit
Acquiring the content information related to the one content from the terminal device used by the user;
The providing unit
13. The information processing apparatus according to claim 12, wherein the predetermined service is provided to the terminal device. The providing unit
14. The information processing apparatus according to claim 12 or 13, wherein an information providing service regarding said similar content is provided. A computer-executed information processing method comprising:
Acquisition for acquiring graph information in which a plurality of nodes corresponding to each of a plurality of contents, which are applications executed on a predetermined device, are linked according to the similarity of the plurality of contents, and content information related to one content process and
a searching step of extracting similar content, which is content similar to the one content among the plurality of contents, by searching the graph information obtained by the obtaining step;
An information processing method characterized by including Acquisition for acquiring graph information in which a plurality of nodes corresponding to each of a plurality of contents, which are applications executed on a predetermined device, are linked according to the similarity of the plurality of contents, and content information related to one content a procedure;
a search procedure for extracting similar content, which is content similar to the one content among the plurality of contents, by searching the graph information acquired by the acquisition procedure;
An information processing program characterized by causing a computer to execute

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