CN115827914A - Method and device for generating relational topological graph, storage medium and electronic equipment - Google Patents
Method and device for generating relational topological graph, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses a method, a device, a storage medium and electronic equipment for generating a relational topological graph. Wherein, the method comprises the following steps: acquiring all scored first video sets of a target object; calculating the similarity between each first video in the first video set and the target video, and finding out a predetermined number of second video sets with the similarity meeting preset requirements with the target video from the first video sets; acquiring a first relation between each object in the object set and a target object and a second relation between a target video and a second video set; and constructing a visualized relational topological graph according to the first relation and the second relation. The method and the device solve the technical problems that the recommendation accuracy is low due to the fact that the recommendation cannot be made according to the video characteristics and the user preference in the related technology, and the association relation among user groups and the similarity relation among similar videos cannot be visually displayed due to the fact that algorithm logic is abstract.
Description
Technical Field
The application relates to the field of intelligent recommendation, in particular to a method, a device, a storage medium and electronic equipment for generating a relational topological graph.
Background
The recommendation method of the related technology is completed by means of a collaborative idea, information of recommended videos is not required to be provided for a system, but recommendation cannot be directly made according to video characteristics and user preferences, so that the recommendation accuracy is low, the algorithm logic is abstract, and the association relationship among user groups and the similarity relationship among similar videos cannot be visually displayed.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a method, a device, a storage medium and electronic equipment for generating a relation topological graph, so as to at least solve the technical problems that recommendation accuracy is low due to the fact that recommendation cannot be made according to video characteristics and user preferences in the related technology, and association relations among user groups and similar relations among similar videos cannot be visually displayed due to the fact that algorithm logics are abstract.
According to an aspect of an embodiment of the present application, there is provided a method for generating a relational topology, including: acquiring all scored first video sets of a target object; calculating the similarity between each first video in the first video set and the target video, and finding out a predetermined number of second video sets with the similarity meeting preset requirements with the target video from the first video sets; acquiring a first relation between each object in the object set and a target object and a second relation between a target video and a second video set; constructing a visualized relational topology graph according to the first relation and the second relation, wherein the relational topology graph at least comprises: and the node corresponding to the video with the highest similarity with the target video in the second video set.
Optionally, after constructing the visualized relationship topology map according to the first relationship and the second relationship, the method further includes: determining the distribution positions of all nodes in the relational topological graph, calling a graph layout algorithm to simulate all the nodes in all the distribution positions into different atoms to optimize the relational topological graph, wherein the nodes correspond to the atoms one by one.
Optionally, invoking a graph layout algorithm to simulate each node at each distribution position as a different atom pair for optimizing the relational topology, including: determining the repulsive force between two atoms and the attractive force between two nodes which are connected with each other in the iterative process; the repulsion force and the attraction force are integrated, the speed of each atom is determined, and the moving distance of each atom during each iteration in the iteration process is determined according to the speed; and iterating the target times until the distance between the atoms meets the preset condition.
Optionally, calculating the similarity between each first video in the first video set and the target video includes: acquiring a historical time period, and carrying out common evaluation on a first video and a target video in a first video set to obtain an object set; determining a first evaluation value and a first average evaluation value of a target object in the object set to the first video, and a second evaluation value and a second average evaluation value of the target object in the object set to the target video; and determining the similarity between the first video and the target video according to the first evaluation value, the second evaluation value, the first average evaluation value and the second average evaluation value.
Optionally, determining the similarity between the first video and the target video according to the first evaluation value, the second evaluation value, the first average evaluation value, and the second average evaluation value includes: determining a first difference value between the first evaluation value and the second average evaluation value and a second difference value between the second evaluation value and the second average evaluation value; and determining the similarity according to the first difference and the second difference.
Optionally, determining the similarity according to the first difference and the second difference includes: acquiring a first product of the first difference and the second difference, and determining a first absolute value corresponding to the first difference and a second absolute value corresponding to the second difference; obtaining a second product of the first absolute value and the second absolute value; and obtaining the similarity according to the ratio of the first product to the second product, wherein the larger the ratio is, the higher the similarity is.
Optionally, calculating the similarity between each first video in the first video set and the target video includes: acquiring an object set which is evaluated by a first video and a target video in a first video set together in a historical period; determining first evaluation text content of a target object in the object set to a first video and second evaluation text content of the target object to the target video; similarity is determined based on the first-rating text content and the second-rating text content.
Optionally, determining the similarity based on the first-evaluation text content and the second-evaluation text content includes: calling a text similarity recognition algorithm, and respectively converting the first evaluation text content and the second evaluation text content into a first vector and a second vector; and calculating cosine similarity between the first vector and the second vector, and determining the similarity based on the cosine similarity, wherein the larger the cosine similarity is, the higher the similarity is.
Optionally, determining the similarity based on the first-evaluation text content and the second-evaluation text content includes: dividing the first evaluation text content and the second evaluation text content into a first sequence string and a second sequence string respectively, wherein the first sequence string comprises: a plurality of first individual character strings, the second sequence string comprising: a plurality of second individual character strings; calculating a first hash value corresponding to each first single character string of the first sequence string to obtain a first hash value array; calculating a second hash value corresponding to each second single character string in the second sequence string to obtain a second hash value array; and determining covariance between the first hash value array and the second hash value array, wherein the larger the absolute value of the covariance is, the higher the similarity is.
Optionally, the visualized relational topology includes: radar chart.
According to another aspect of the embodiments of the present application, there is also provided an apparatus for generating a relational topology, including: the first acquisition module is used for acquiring all scored first video sets of the target object; the calculating module is used for calculating the similarity between each first video in the first video set and the target video and finding out a predetermined number of second video sets with the similarity meeting the preset requirement with the target video from the first video sets; the second acquisition module is used for acquiring a first relation between each object in the object set and the target object and a second relation between the target video and the second video set; the determining module is used for constructing a visualized relationship topological graph according to the first relationship and the second relationship, wherein the relationship topological graph at least comprises: and the node corresponding to the video with the highest similarity with the target video in the second video set.
According to another aspect of embodiments of the present application, there is also provided a nonvolatile storage medium including: the storage medium comprises a stored program, wherein when the program runs, the device on which the storage medium is positioned is controlled to execute any method for generating the relationship topological graph.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement any of the methods of generating a relational topology graph.
In the embodiment of the application, a visual relation topological graph is constructed by determining a video set according to similarity, and all scored first video sets of a target object are obtained; calculating the similarity between each first video in the first video set and the target video, and finding out a predetermined number of second video sets with the similarity meeting preset requirements with the target video from the first video sets; acquiring a first relation between each object in the object set and a target object and a second relation between a target video and a second video set; the method has the advantages that the visual relation topological graph is constructed according to the first relation and the second relation, the purpose of accurately recommending videos is achieved, the technical effects of visually displaying the association relation among the user groups and the similar relation among the similar videos are achieved, and the technical problems that the recommendation accuracy is low due to the fact that recommendation cannot be made according to video characteristics and user preferences in the related technology, and the association relation among the user groups and the similar relation among the similar videos cannot be visually displayed due to the fact that algorithm logic is abstract are solved.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flowchart illustrating a method for generating a relational topology according to an embodiment of the present disclosure;
FIG. 2 is a schematic illustration of a radar chart according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an apparatus for generating a relational topology according to an embodiment of the present application;
fig. 4 is a schematic block diagram of an example electronic device 400 in accordance with an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be implemented in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present application, there is provided a method embodiment for generating a relational topology, it being noted that the steps illustrated in the flowchart of the figure can be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described can be performed in an order different than here.
Fig. 1 is a method for generating a relationship topology according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
step S102, acquiring all scored first video sets of a target object;
step S104, calculating the similarity between each first video in the first video set and the target video, and finding out a predetermined number of second video sets with the similarity meeting preset requirements with the target video from the first video sets;
it should be noted that the preset rule is to sort the videos according to the similarity, and select a predetermined number of videos from high to low.
Step S106, acquiring a first relation between each object in the object set and the target object and a second relation between the target video and the second video set;
step S108, constructing a visualized relational topological graph according to the first relation and the second relation, wherein the relational topological graph at least comprises: and the node corresponding to the video with the highest similarity with the target video in the second video set.
It is understood that, when the predetermined number is K, the second set of videos may represent a set of K videos by N (u, i).
In the embodiment of the application, a mode of determining a video set according to similarity so as to construct a relation topological graph is adopted, and all scored first video sets of a target object are obtained; calculating the similarity between each first video in the first video set and the target video, and finding out a predetermined number of second video sets with the similarity meeting preset requirements with the target video from the first video sets; acquiring a first relation between each object in the object set and a target object and a second relation between a target video and a second video set; the method has the advantages that the visual relation topological graph is constructed according to the first relation and the second relation, the purpose of accurately recommending videos is achieved, the technical effects of visually displaying the association relation among the user groups and the similar relation among the similar videos are achieved, and the technical problems that the recommendation accuracy is low due to the fact that recommendation cannot be made according to video characteristics and user preferences in the related technology, and the association relation among the user groups and the similar relation among the similar videos cannot be visually displayed due to the fact that algorithm logic is abstract are solved.
In some optional embodiments of the present application, after constructing the visualized relationship topology map according to the first relationship and the second relationship, the method further includes: determining the distribution positions of all nodes in the relational topological graph, calling a graph layout algorithm to simulate all the nodes in all the distribution positions into different atoms to optimize the relational topological graph, wherein the nodes correspond to the atoms one by one.
It should be noted that the graphic layout algorithm is an FR algorithm.
As an optional implementation, invoking a graph layout algorithm to simulate each node on each distribution position as a different atom to optimize the relational topology, including: determining repulsion between two atoms in an iterative process and attraction between two nodes connected with each other; the repulsion force and the attraction force are integrated, the speed of each atom is determined, and the moving distance of each atom during each iteration in the iteration process is determined according to the speed; and iterating the target times until the distance between atoms meets a preset condition.
It should be noted that the preset condition is to achieve an ideal layout effect among the atoms.
It is understood that the number of iterations is set according to the layout effect.
In an exemplary embodiment of the present application, calculating a similarity between each first video in the first video set and the target video includes: acquiring a history period, and carrying out common evaluation on a first video and a target video in a first video set to obtain an object set; determining a first evaluation value and a first average evaluation value of a target object in the object set to the first video, and a second evaluation value and a second average evaluation value of the target object in the object set to the target video; and determining the similarity between the first video and the target video according to the first evaluation value, the second evaluation value, the first average evaluation value and the second average evaluation value.
As an optional implementation, determining the similarity between the first video and the target video according to the first evaluation value, the second evaluation value, the first average evaluation value, and the second average evaluation value includes: determining a first difference value between the first evaluation value and the second average evaluation value and a second difference value between the second evaluation value and the second average evaluation value; and determining the similarity according to the first difference and the second difference.
As an optional implementation, determining the similarity according to the first difference and the second difference includes: acquiring a first product of the first difference and the second difference, and determining a first absolute value corresponding to the first difference and a second absolute value corresponding to the second difference; obtaining a second product of the first absolute value and the second absolute value; and obtaining the similarity according to the ratio of the first product to the second product, wherein the larger the ratio is, the higher the similarity is.
In some optional embodiments of the present application, calculating a similarity between each first video in the first video set and the target video includes: acquiring an object set which is evaluated by a first video and a target video in a first video set together in a historical period; determining first evaluation text content of a target object in the object set to a first video and second evaluation text content of the target object to the target video; and determining similarity based on the first evaluation text content and the second evaluation text content.
In some optional embodiments of the present application, determining a similarity based on the first-evaluation text content and the second-evaluation text content includes: calling a text similarity recognition algorithm, and respectively converting the first evaluation text content and the second evaluation text content into a first vector and a second vector; and calculating cosine similarity between the first vector and the second vector, and determining the similarity based on the cosine similarity, wherein the larger the cosine similarity is, the higher the similarity is.
It should be noted that the algorithm for converting the text content into the vector includes, but is not limited to: word2vec algorithm.
It can be understood that the similarity of the video can be determined according to the text content evaluated by the video, and the text content can be converted into a vector, so that the text content can be quantized according to the vector.
In some optional embodiments of the present application, determining a similarity based on the first-evaluation text content and the second-evaluation text content includes: dividing the first evaluation text content and the second evaluation text content into a first sequence string and a second sequence string respectively, wherein the first sequence string comprises: a plurality of first individual character strings, the second sequence string comprising: a plurality of second individual character strings; calculating a first hash value corresponding to each first single character string of the first sequence string to obtain a first hash value array; calculating a second hash value corresponding to each second single character string in the second sequence string to obtain a second hash value array; determining covariance between the first hash value array and the second hash value array, wherein the greater the absolute value of the covariance, the higher the similarity.
It can be understood that the similarity of the video can be determined according to the text content evaluated by the video, the text content is divided into sequence strings, the covariance between the hash value arrays is determined, the video similarity is determined according to the covariance, and therefore the text content is quantized according to the covariance.
As an optional implementation, the visualized relational topology includes: radar chart.
The principle of the FR algorithm is described below:
(1) Setting the distribution position of the initial node;
(2) Calculating the repulsion between two nodes in the local region during each iteration;
(3) Calculating the gravitation between the nodes connected by the edges during each iteration;
(4) After the repulsion force and the attraction force are integrated, the speed of each node is determined, and the distance which each node should move in each generation is determined through the speed;
(5) And (4) iterating the target times, wherein the moving distance of each node is gradually reduced each time until an ideal layout effect is achieved.
It should be noted that the iteration number is set according to the layout effect, and the moving distance and the operation time are respectively in direct proportion to the ideal degree of the layout effect.
In the above step, it is assumed that the height of the region is H, the width is W, the position of the initial node is G = (V, E), V represents a set of nodes, and E represents an edge between nodes. Each node has two layout parameters, namely position pos and displacement generated under the influence of resultant force, and the parameter variables used by the algorithm are calculated as follows:
area of the display area:
area＝W*H；
wherein the height is H and the width is W.
Optimal distance between nodes:
where | V | represents the number of nodes in the graph, and area is the area of the region.
Geometric distance between nodes u and v:
pos is position information, and u and v represent displacement information of nodes u and v respectively.
Attraction between adjacent nodes:
wherein d is the geometric distance between the nodes u and v, and k is the optimal distance between the nodes.
Repulsive force between nodes:
wherein d is the geometric distance between the nodes u and v, and k is the optimal distance between the nodes.
In conclusion, a relational topology is obtained.
Fig. 2 is a schematic diagram of a radar map according to an embodiment of the present application, and as shown in fig. 2, a radar map may be generated by using a radar map visualization algorithm and a force guidance algorithm of a network topology.
Fig. 3 is a schematic structural diagram of an apparatus for generating a relationship topology according to an embodiment of the present application, and as shown in fig. 3, the apparatus includes:
the first obtaining module 30 is configured to obtain all scored first video sets of the target object;
the calculating module 32 is configured to calculate similarity between each first video in the first video sets and the target video, and find a predetermined number of second video sets, of which the similarity with the target video meets a preset requirement, from the first video sets;
a second obtaining module 34, configured to obtain a first relationship between each object in the object set and the target object, and a second relationship between the target video and the second video set;
a determining module 36, configured to construct a visualized relationship topology graph according to the first relationship and the second relationship, where the relationship topology graph at least includes: and the node corresponding to the video with the highest similarity with the target video in the second video set.
In the device, a first obtaining module 30 is configured to obtain all scored first video sets of a target object; the calculating module 32 is configured to calculate similarity between each first video in the first video sets and the target video, and find a predetermined number of second video sets, of which the similarity with the target video meets a preset requirement, from the first video sets; a second obtaining module 34, configured to obtain a first relationship between each object in the object set and the target object, and a second relationship between the target video and the second video set; the determining module 36 is configured to construct a visual relationship topological graph according to the first relationship and the second relationship, so as to achieve the purpose of improving accurate video recommendation, thereby achieving the technical effect of visually displaying the association relationship between the user groups and the similarity relationship between the similar videos, and further solving the technical problems that the recommendation accuracy is low due to the fact that recommendation cannot be made according to video characteristics and user preferences in the related art, and the association relationship between the user groups and the similarity relationship between the similar videos cannot be visually displayed due to the fact that algorithm logic is relatively abstract.
According to another aspect of the embodiments of the present application, a non-volatile storage medium is further provided, where the non-volatile storage medium includes a stored program, and when the program runs, a device in which the non-volatile storage medium is located is controlled to execute any one of the methods for generating the relationship topology map.
Specifically, the storage medium is used for storing program instructions of the following functions, and the following functions are realized:
acquiring all scored first video sets of a target object; calculating the similarity between each first video in the first video set and the target video, and finding out a predetermined number of second video sets with the similarity meeting preset requirements with the target video from the first video sets; acquiring a first relation between each object in the object set and a target object and a second relation between a target video and a second video set; and constructing a visualized relational topological graph according to the first relation and the second relation.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the aforementioned storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the aforementioned.
In an exemplary embodiment of the present application, there is also provided a computer program product, including a computer program, which when executed by a processor, implements any of the above methods for generating a relational topology.
Optionally, the computer program may, when executed by a processor, implement the steps of:
acquiring all scored first video sets of a target object; calculating the similarity between each first video in the first video set and the target video, and finding out a predetermined number of second video sets with the similarity meeting preset requirements with the target video from the first video sets; acquiring a first relation between each object in the object set and a target object and a second relation between a target video and a second video set; and constructing a visualized relational topological graph according to the first relation and the second relation.
An embodiment according to the present application provides an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform any one of the above methods for generating a relational topology.
Optionally, the electronic device may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Fig. 4 is a schematic block diagram of an example electronic device 400 in accordance with an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 4, the apparatus 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the device 400 can also be stored. The calculation unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.
Claims (13)
1. A method of generating a relational topology, comprising:
acquiring all scored first video sets of a target object;
calculating the similarity between each first video in the first video set and a target video, and finding out a predetermined number of second video sets with the similarity meeting preset requirements with the target video from the first video sets;
acquiring a first relation between each object in an object set and the target object and a second relation between the target video and the second video set;
constructing a visualized relationship topological graph according to the first relationship and the second relationship, wherein the relationship topological graph at least comprises: and the node corresponding to the video with the highest similarity with the target video in the second video set.
2. The method of claim 1, wherein after constructing the visualized relationship topology graph from the first relationship and the second relationship, the method further comprises:
determining the distribution positions of all nodes in the relational topological graph, calling a graph layout algorithm to simulate all the nodes in all the distribution positions into different atoms, and optimizing the relational topological graph, wherein the nodes correspond to the atoms one by one.
3. The method of claim 2, wherein the optimizing the relational topology by invoking a graph layout algorithm to model each node at each distribution location as a different atom comprises:
determining repulsion between two atoms in an iterative process and attraction between two nodes connected with each other;
integrating the repulsion force and the attraction force to determine the speed of each atom, and determining the moving distance of each atom during each iteration in the iteration process according to the speed;
and iterating the target times until the distance between the atoms meets a preset condition.
4. The method of claim 1, wherein calculating the similarity between each first video in the first video set and a target video comprises:
acquiring a history period, and carrying out common evaluation on the first video and the target video in a first video set to obtain an object set;
determining a first evaluation value and a first average evaluation value of a target object in the object set to the first video, and a second evaluation value and a second average evaluation value of the target object to the target video;
and determining the similarity between the first video and the target video according to the first evaluation value, the second evaluation value, the first average evaluation value and the second average evaluation value.
5. The method according to claim 4, wherein determining the similarity between the first video and the target video according to the first evaluation value, the second evaluation value, the first average evaluation value, and the second average evaluation value comprises:
determining a first difference value between the first evaluation value and the second average evaluation value, and a second difference value between the second evaluation value and the second average evaluation value;
and determining the similarity according to the first difference and the second difference.
6. The method of claim 5, wherein determining the similarity from the first difference and the second difference comprises:
acquiring a first product of the first difference and the second difference, and determining a first absolute value corresponding to the first difference and a second absolute value corresponding to the second difference;
acquiring a second product of the first absolute value and the second absolute value;
and obtaining the similarity according to the ratio of the first product to the second product, wherein the larger the ratio is, the higher the similarity is.
7. The method of claim 1, wherein calculating the similarity between each first video in the first video set and a target video comprises:
acquiring an object set which is evaluated by the first video and the target video together in a first video set in a historical period;
determining first evaluation text content of a target object in the object set to the first video and second evaluation text content of the target video;
determining the similarity based on the first and second rated text contents.
8. The method of claim 7, wherein determining the similarity based on the first-rating text content and the second-rating text content comprises:
calling a text similarity recognition algorithm, and respectively converting the first evaluation text content and the second evaluation text content into a first vector and a second vector;
calculating cosine similarity between the first vector and the second vector, and determining the similarity based on the cosine similarity, wherein the greater the cosine similarity, the higher the similarity.
9. The method of claim 7, wherein determining the similarity based on the first-rating text content and the second-rating text content comprises:
dividing the first and second comment text contents into a first sequence string and a second sequence string, respectively, wherein the first sequence string includes: a plurality of first single strings, the second sequence of strings comprising: a plurality of second individual character strings;
calculating a first hash value corresponding to each first single character string of the first sequence string to obtain a first hash value array;
calculating a second hash value corresponding to each second single character string in the second sequence string to obtain a second hash value array;
determining a covariance between the first hash value array and the second hash value array, wherein the greater an absolute value of the covariance is, the higher the similarity is.
10. The method of any one of claims 1 to 9, wherein the visualized relational topology graph comprises: radar chart.
11. An apparatus for generating a relational topology, comprising:
the first acquisition module is used for acquiring all scored first video sets of the target object;
the calculating module is used for calculating the similarity between each first video in the first video set and the target video and finding out a predetermined number of second video sets with the similarity meeting preset requirements with the target video from the first video sets;
a second obtaining module, configured to obtain a first relationship between each object in an object set and the target object, and a second relationship between the target video and the second video set;
a determining module, configured to construct a visualized relationship topology graph according to the first relationship and the second relationship, where the relationship topology graph at least includes: and the node corresponding to the video with the highest similarity with the target video in the second video set.
12. A non-volatile storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, a device in which the storage medium is located is controlled to execute the method for generating a relational topology according to any one of claims 1 to 10.
13. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of generating a relational topology as claimed in any one of claims 1 to 10.
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