CN111461785A

CN111461785A - Content value attribute evaluation method and device and copyright trading platform

Info

Publication number: CN111461785A
Application number: CN202010250406.0A
Authority: CN
Inventors: 黄凯明; 杨磊
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2020-04-01
Filing date: 2020-04-01
Publication date: 2020-07-28

Abstract

The embodiment of the specification discloses a method and a device for evaluating content value attributes, a copyright trading platform and a computer readable storage medium. The method comprises the following steps: acquiring user preference data about a plurality of contents, wherein the plurality of contents comprise contents to be evaluated; screening out contents similar to the contents to be evaluated from the plurality of contents according to user preference data about the plurality of contents; estimating the value of the content to be evaluated based on the transaction value attribute of the content to be evaluated and the transaction value attribute of the content similar to the content to be evaluated; resetting the value attribute for the content to be evaluated according to the value of the content to be evaluated; storing the value attribute of the content to be evaluated in a block chain evidence storage platform; and displaying the value attribute of the content to be evaluated to the user terminal along with the content to be evaluated.

Description

Content value attribute evaluation method and device and copyright trading platform

Technical Field

The present specification relates to a blockchain technology, and more particularly, to a method of evaluating a content value attribute, an apparatus for evaluating a content value attribute, a copyright trading platform, and a computer-readable storage medium.

Background

With the increasing popularity of copyright concepts, users are becoming more and more accustomed to paying for content, such as photographic works, literary works, and musical works. How to help content creators to position the content created by the content creators through technical means and to realize the content value becomes a technical problem to be solved urgently.

Disclosure of Invention

Embodiments disclosed herein provide an evaluation scheme for content value attributes.

According to a first aspect disclosed in the present specification, there is provided a method of evaluating a content value attribute, comprising the steps of:

obtaining user preference data about a plurality of contents, wherein the plurality of contents comprise contents to be evaluated;

according to the user preference data of the plurality of contents, screening out contents similar to the contents to be evaluated from the plurality of contents;

estimating the value of the content to be evaluated based on the transaction value attribute of the content to be evaluated and the transaction value attribute of the content similar to the content to be evaluated;

resetting a value attribute for the content to be evaluated according to the value of the content to be evaluated;

storing the value attribute of the content to be evaluated in a block chain evidence storage platform;

and displaying the value attribute of the content to be evaluated to a user terminal along with the content to be evaluated.

Optionally, the method further comprises:

calculating weights of different creators based on transaction value attributes generated by the same user supporting the contents of different creators;

and adjusting the value of the content to be evaluated according to the weight of the creator of the content to be evaluated.

Optionally, calculating weights for different authors based on trading value attributes of content that the same user supports different authors, comprises:

constructing a directed graph among a plurality of creators based on transaction value attributes generated by the same user supporting the contents of different creators;

and calculating the weights of the plurality of creators by using a webpage ranking algorithm according to the directed graph among the plurality of creators.

Optionally, the user preference data comprises the following types of data:

data of contents displayed to a user, data of contents clicked by the user, data of contents collected by the user, data of contents purchased by the user and data of contents evaluated by the user;

different types of user preference data have respective weights when determining content similar to the content to be evaluated.

Optionally, the weights of the different types of user preference data are determined based on the contribution degree of the user preference data of the types to the purchasing behavior.

Optionally, the screening, according to the user preference data on the plurality of contents, contents similar to the content to be evaluated from the plurality of contents includes:

and according to the user preference data of the plurality of contents, calculating the user preference similarity of the contents to be evaluated and other contents, and determining the contents with the user preference similarity of the contents to be evaluated within a preset threshold as the contents similar to the contents to be evaluated.

Optionally, calculating user preference similarities of the content to be evaluated and other content according to the user preference data about the plurality of contents, and determining the content with the user preference similarity of the content to be evaluated within a preset threshold as the content similar to the content to be evaluated, includes:

determining the preference degree of a single user to the content according to the preference data of the single user to the single content and the weight of the preference data;

constructing a user preference matrix of a plurality of users for a plurality of contents, and generating a co-occurrence matrix based on the user preference matrix;

and calculating cosine similarity between the contents by using the co-occurrence matrix, and determining the contents with the cosine similarity within a preset threshold value as the contents similar to the contents to be evaluated.

Optionally, the method further comprises:

obtaining new content, the new content being content without user preference data;

and configuring categories for the new content, and determining the initial value attribute of the new content according to the value attribute of the content of the same category.

Optionally, configuring a category for the new content, including:

roughly classifying the new content according to the title, the label and the description information of the new content;

and determining a fine classification to which the new content belongs through a clustering algorithm.

Optionally, the method further comprises:

and releasing the content and the information of the content creator on the blockchain evidence storage platform.

According to a second aspect disclosed in the present specification, there is provided an evaluation apparatus of a content value attribute, comprising the following modules:

the system comprises a user preference data acquisition module, a content evaluation module and a content evaluation module, wherein the user preference data acquisition module is used for acquiring user preference data of a plurality of contents, and the contents comprise contents to be evaluated;

a similar content determination module, configured to screen, according to the user preference data regarding the plurality of contents, a content similar to the content to be evaluated from the plurality of contents;

the value estimation module is used for estimating the value of the content to be evaluated based on the transaction value attribute of the content to be evaluated and the transaction value attribute of the content similar to the content to be evaluated;

the value attribute resetting module is used for resetting the value attribute for the content to be evaluated according to the value of the content to be evaluated;

the first storage module is used for storing the value attribute of the content to be evaluated in a block chain evidence storage platform;

and the display module is used for displaying the value attribute of the content to be evaluated to the user terminal along with the content to be evaluated.

Optionally, the apparatus further comprises:

the creator weight determining module is used for calculating the weights of different creators based on the transaction value attribute generated by the same user supporting the contents of different creators;

and the value adjusting module is used for adjusting the value of the content to be evaluated according to the weight of the creator of the content to be evaluated.

Optionally, the apparatus further comprises:

the new content acquisition module is used for acquiring new content, and the new content is content without user preference data;

the new content category configuration module is used for configuring categories for the new content;

and the new content value attribute determining module is used for determining the initial value attribute of the new content according to the value attribute of the content of the same category.

Optionally, the apparatus further comprises:

and the second storage module is used for releasing the content and the information of the content creator on the block chain evidence storage platform.

According to a third aspect disclosed in the present specification, there is provided an apparatus for evaluating a value attribute of content, comprising a processor and a memory; the memory has stored therein instructions that, when executed by the processor, implement the method of assessing a value attribute of content as set forth in any of the preceding claims.

According to a fourth aspect of the disclosure, a copyright trading platform is provided, which comprises the content value attribute evaluation device and the blockchain evidence storage platform.

According to a fifth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor, implement the method of evaluating a content value attribute of any one of the preceding claims.

The method for evaluating the content value attribute in the embodiment of the specification can evaluate the value of the content to be evaluated by integrating the transaction value attribute of the content to be evaluated and the transaction value attribute of the similar content, can well reflect the real value of the content to be evaluated, and promotes the benign development of the platform creation ecology.

Features of embodiments of the present specification and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description, serve to explain the principles of the embodiments of the specification.

Fig. 1 is a block diagram of a copyright transaction system provided by an embodiment of the present specification;

FIG. 2 is a flow diagram of a method for evaluating a value attribute of content provided by one embodiment of the present description;

FIG. 3 is a schematic diagram of a directed graph provided by one embodiment of the present description;

FIG. 4 is a block diagram of an apparatus for evaluating a value attribute of content provided in one embodiment of the present specification;

FIG. 5 is a block diagram of an apparatus for evaluating a value attribute of content provided in one embodiment of the present specification;

FIG. 6 is a block diagram of an apparatus for evaluating a value attribute of content provided in one embodiment of the present specification;

fig. 7 is a block diagram of an apparatus for evaluating a content value attribute provided in one embodiment of the present specification.

Detailed Description

Various exemplary embodiments of the present specification will now be described in detail with reference to the accompanying drawings.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the embodiments, their application, or uses.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

< copyright management System >

Fig. 1 is a block diagram of a copyright transaction system provided in an embodiment of the present specification. As shown in fig. 1, the copyright management system includes a copyright trading platform and a plurality of terminal devices 103 of content creators, which are communicatively connected via a network 102.

The copyright trading platform may include, for example, a server, and the configuration of the server may include, but is not limited to: processor, memory, interface device, communication device, input device, output device. The processor may include, but is not limited to, a central processing unit CPU, a microprocessor MCU, or the like. The memory may include, but is not limited to, ROM (read only memory), RAM (random access memory), non-volatile memory such as a hard disk, and the like. The interface means may include, but is not limited to, a USB interface, a serial interface, a parallel interface, etc. The communication means is capable of wired or wireless communication, for example, and may specifically include WiFi communication, bluetooth communication, 2G/3G/4G/5G communication, and the like. Input devices include, but are not limited to, a keyboard, a mouse, and the like. Output devices include, but are not limited to, display screens and the like. The server may be configured to include only some of the above devices.

Terminal equipment 103 may be, for example, an electronic device installed with a smart operating system (e.g., android, IOS, Windows, L inux, etc. systems), including but not limited to laptop, desktop computer, cell phone, tablet, etc. the configuration of terminal equipment 103 includes, but is not limited to, processor 1031, memory 1032, interface device 1033, communication device 1034, GPU (Graphics Processing Unit, image processor) 1035, display device 1036, input device 1037, speaker 1038, microphone 1039, and camera 1030, processor 1031 includes, but is not limited to, a central processor CPU, microprocessor MCU, etc., memory 1032 includes, but is not limited to, ROM (read only memory), RAM (random access memory), non-volatile memory such as a hard disk, etc. interface device 1033 includes, but is not limited to, a USB interface, a serial interface, a parallel interface, etc. communication device is, for example, capable of wired or wireless communication, and may specifically include, WiFi communication, bluetooth communication, 2G/3G/4G/5G communication, etc. the communication device may include, a mouse screen, etc. the terminal equipment may also include, but is not limited to, a touch screen, etc. the aforementioned configuration.

In one embodiment applied to the present specification, a content creator may upload content created by itself to a copyright trading platform through the terminal device 103, and the copyright trading platform evaluates the value of the content and helps to sell the content. The buyer can browse, search and buy the content of the self-mind device from the copyright trading platform, etc. Specifically, the copyright trading platform can expose the content to the user through the terminal device, namely show the content to the user, and the user can collect the content, click to check the content, purchase the content and evaluate the content through the terminal device. The content referred to in this specification includes, but is not limited to, literary works, photographic works, pictorial works, audio-visual works, and the like.

In one embodiment, a block chain storage and certification platform and an evaluation device 101 of content value attribute can be included in the copyright trading platform.

The block chain (Blockchain) technology is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. The block chain evidence storage platform is a service platform for storing evidence of data uploaded by a user based on a block chain technology, and the user can store the information needing to be preserved by using the block chain evidence storage platform. In this embodiment of the present specification, after receiving the content uploaded by the creator, the copyright transaction platform issues and stores information such as the content, the creator of the content, and the uploading time in the blockchain storage platform. In addition, data of copyright transaction, such as transaction data of purchasers, purchased contents, transaction prices and transaction time and the like, can also be issued and stored in the blockchain evidence storing platform so as to record the circulation process of the copyright of the evidence storing contents.

The evaluation means 101 of the content value attribute is used to evaluate the value of the content. The evaluation apparatus 101 of the content value attribute may be, for example, a server, and the configuration of the server may include, but is not limited to: processor 1011, memory 1012, interface 1013, communication device 1014, input device 1015, output device 1016. The processor 1011 may include, but is not limited to, a central processing unit CPU, a microprocessor MCU, or the like. The memory 1012 may include, but is not limited to, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. Interface device 1013 may include, but is not limited to, a USB interface, a serial interface, a parallel interface, and the like. The communication device 1014 is capable of wired or wireless communication, for example, and may specifically include WiFi communication, bluetooth communication, 2G/3G/4G/5G communication, and the like. Input devices 1015 include, but are not limited to, a keyboard, a mouse, and the like. Output device 1016 includes, but is not limited to, a display screen or the like. The server may be configured to include only some of the above devices.

The rights management system shown in FIG. 1 is illustrative only and is in no way intended to suggest any limitation as to the embodiment of the specification, its application, or use. It should be understood by those skilled in the art that although the copyright trading platform, the evaluation device of the content value attribute and a plurality of devices of the terminal equipment are described in the foregoing, the embodiments of the present specification may only refer to some of the devices. For example, the evaluation means of the content value attribute may involve only the processor, the memory, and the communication means, and the terminal device may involve only the processor, the memory, the communication means, and the display screen. Those skilled in the art can design instructions based on the disclosed embodiments of the present specification. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.

< evaluation method of content value Attribute >

Firstly, a preprocessing process of the copyright trading platform is introduced after an author uploads a new content to the copyright trading platform.

S202, after the content is uploaded to the copyright trading platform by the creator, the copyright trading platform checks the content to filter out illegal and forbidden content.

The content uploaded by the creator can be literary works, photographic works, pictorial works, audio-video works and the like.

After the creator uploads the content to the copyright trading platform, the copyright trading platform checks and filters the content, and the content related to illegal and forbidden conditions such as pornography, terrorism and the like is excluded and is not released.

And S204, after the content passes the audit, classifying the content by the copyright trading platform, namely setting the category to which the content belongs.

In one embodiment, the content may be classified primarily, i.e., roughly, according to its title, tag, and description information. And further classifying the content through a clustering algorithm to determine a fine classification to which the content belongs. The clustering algorithm is not limited to the clustering algorithm based on density, hierarchy and the like.

For example, a newly uploaded photo is roughly classified into the "nature scene" category according to photo tags. And clustering the newly uploaded photo with the pictures which belong to the large category of the natural scene and are already classified finely by using the pictures in the copyright library so as to determine the fine category to which the newly uploaded photo belongs, wherein the fine category of the newly uploaded photo is the sea.

In one specific example, the classification process may employ a nearest neighbor classification algorithm (KNN) based classification method. In one specific example, the classification process may employ a classification method based on a Singular Value Decomposition (SVD) algorithm.

And S206, storing the content and recording the category of the content.

In step S206, information such as the content, the creator of the content, and the time when the creator uploads the content may be published and stored on the blockchain voucher platform. By the technology, the author only needs to upload the content to the copyright trading platform in time after authoring the content, and can powerfully prove the identity of the author of the content.

After the preprocessing, an initial value attribute is evaluated for the newly uploaded content. For new content, the copyright trading platform has no relevant historical data, and the initial value attribute of the new content is determined by referring to the value attributes of the contents of the same category. For example, the average value of the value attributes of the same category of contents is used as the initial value attribute of the new content. And displaying the initial value attribute of the new content to the user terminal along with the new content.

The value attribute of the content may be a numerical value that can embody the value of the content, such as the number of points required for the content to be acquired. For example, for a small word, its value attribute may be 5 value points per thousand words, i.e., the value of each thousand words is 5 value points. The user can obtain the value points through purchasing, point exchanging and other modes, so that the right of browsing the novel is obtained. For example, the value attribute of the content may be a price of the content. For example, for a new picture classified as "nature scene" - "sea", the initial price is the average price of other pictures in the copyright trading platform that are also "nature scene" - "sea".

After the creator uploads the content to the copyright trading platform, the copyright trading platform releases the content, and a user can access the copyright trading platform through the terminal device to browse, collect, purchase and evaluate the content. These operations by the user are recorded, and the evaluation device 101 of the content value attribute can evaluate the value of the content based on these history data. Referring to FIG. 2, a method for evaluating the value of content that already has historical data is described, including steps S302-312.

S302, user preference data of a plurality of contents are obtained, and the plurality of contents comprise contents to be evaluated.

For content, the user preference data includes at least one of the following types of data:

(1) the content is presented to the user's data. The content is displayed to the user, and local information such as title, introduction, thumbnail and the like of the content is displayed to the user. For example, a user searches for "martial arts novel," and the search results page may present the user with a brief introduction to the searched novel.

(2) The user clicks on the data of the content.

(3) The user collects data for the content.

(4) The user purchases the data of the content.

(5) The user evaluates the data of the content.

The user preference data of the content can reflect the interest of the user in the content and belongs to user-item data. The user preference data relates to the user's interest in, and opinion of, the content. The combination of various user preference data can reflect the preference of the user to the content more accurately.

S304, according to the user preference data of the plurality of contents, the contents similar to the contents to be evaluated are screened out from the plurality of contents.

Different types of user preference data differ in the ability to reflect user preferences, for example, a user purchasing content may be more likely to fully reflect that the user likes the content than a user clicking to view the content. Based on this, when content similar to the content to be evaluated is screened out from the plurality of contents, different types of user preference data have respective weights.

In a specific example, the weights of the different types of user preference data are determined based on the contribution of the types of user preference data to the purchasing behavior. For example, through big data statistical analysis of the copyright trading platform, it is found that a purchase behavior is likely to be generated when a behavior of clicking and viewing one content by a user reaches more than 5 times, and then the contribution degree of the data of clicking the content by the user to the purchase behavior of the user may be 0.2. For example, for content distributed in a serial form, through big data statistical analysis of a copyright trading platform, it is found that if a user evaluates the content more than 4 times, the user will continuously purchase the content, and the contribution degree of the data of the content evaluated by the user to the user purchasing behavior may be 0.25. In the case where the weight of the data of the content purchased by the user is 1, the weight of the data of the content clicked by the user may be 0.1, and the weight of the data of the content evaluated by the user may be 0.25.

In one specific example, the weights for the various types of user preference data are as follows:

(1) the data that the content is presented to the user may correspond to the lowest weight, for example 0.05.

(2) The user clicks on the data of the content, which may correspond to a lower weight, e.g. 0.1.

(3) The data of the user's favorite contents may correspond to a higher weight, for example, 0.15.

(4) The data of the content purchased by the user, which is the data that can best embody the user preference, may correspond to the highest weight, for example, 0.5.

(5) The user rating the data of the content may correspond to a higher weight, for example 0.2.

In the embodiment of the present specification, the user preference similarity of the content to be evaluated and other content is calculated according to the user preference data on a plurality of contents, and the content having the user preference similarity to the content to be evaluated within a preset threshold is determined as the content similar to the content to be evaluated. In this step, the similarity between the content and the content (item-item) may be determined using an item-based collaborative filtering algorithm. The collaborative filtering is to recommend information interested by users by using the preferences of groups with mutual interests and common experiences, and can be used for recommending articles. The principle of the collaborative filtering algorithm based on items is that the reason why one item has a greater similarity to another item is because users who like the item mostly like the other item. For example, user x likes item a and item C, user y likes item a, item B, and item C, and user z likes item a, item C, and item D, and from these user preference data, it can be considered that users who like item a all like item C, and item a and item C may be relatively similar.

In a specific example, calculating the user preference similarity of the content to be evaluated and other content according to the user preference data about a plurality of contents, and determining the content with the user preference similarity to the content to be evaluated within a preset threshold as the content similar to the content to be evaluated may include the following steps: according to preference data of a user to a content and the weight of the preference data, the preference degree of the user to the content can be calculated; the method comprises the steps of constructing a user preference matrix of a plurality of users for a plurality of contents, generating a co-occurrence matrix by using the user preference matrix, calculating cosine similarity between the contents by using the co-occurrence matrix, and determining the contents with the cosine similarity within a preset threshold value as the contents similar to the contents to be evaluated.

In another embodiment, the similarity between the content and the content may be calculated in a manner other than steps S302 and S304. For example, the similarity between the content to be evaluated and other content is calculated by adopting a text similarity algorithm for the literary works, and the similarity between the content to be evaluated and other content is calculated by adopting a picture similarity algorithm for the picture works.

S306, estimating the value of the content to be evaluated based on the transaction value attribute of the content to be evaluated and the transaction value attribute of the content similar to the content to be evaluated.

The transaction value attribute of the content may be a value attribute corresponding to the content when the content is actually successfully transacted. The trading value attribute of the content may be a price corresponding to the actual trading behavior of the content.

Transaction actions may include, but are not limited to: the author authorizes content he or she authored to be viewed by a user, authorizes content he or she authored to be commercially used by a user, authorizes exclusive commercial use by a user, transfers ownership of content to a user, authorizes a user to be able to modify and use content, and so forth.

For an intangible product such as content, the trading value attribute does not fully represent the value of the content. For example, the artistic level of a work may be high for a new creator, but since the creator is a new person, the transaction value attribute of the previous period is low, and the transaction value attribute cannot fully represent the value of the work. In addition, the value of the content may vary, the market conditions may fluctuate, and the reputation of the creator may change, all of which may affect the value of the content.

In the embodiment of the specification, after the content is subjected to trading, value evaluation can be performed on the content again, so that the value attribute is more reasonable and more accords with market conditions.

In order to accurately grasp the value of the content, help the creator accurately locate and better render the content, the value of the content to be evaluated is estimated using the trading value attributes of the content similar to the content to be evaluated together with the trading value attributes of the content to be evaluated.

In one embodiment, the value of the content to be evaluated is calculated according to the formula V1 ═ N1 × a + N2 × (1-a), where N1 is a trading value attribute of the content to be evaluated, N2 is an average trading value attribute of a plurality of contents similar to the content to be evaluated, V1 is the value of the content to be evaluated, a is a preset parameter and is set between 0 and 1.

After the value of the content to be evaluated is estimated, the process proceeds to step S308, and the value attribute is reset for the content to be evaluated according to the value of the content to be evaluated. Or, in an embodiment, after the value of the content to be evaluated is estimated, the value of the content to be evaluated is further adjusted, and then the process proceeds to step S308, so as to reset the value attribute for the content to be evaluated according to the adjusted value of the content to be evaluated.

Further adjustments to the value of the content to be evaluated may include steps S3062-S3064.

S3062, calculating weights of different creators based on trading value attributes generated by the same user supporting the contents of different creators.

In one particular example, a directed graph between multiple creators is constructed based on transactional value attributes generated by multiple same users supporting different creators' content. The user support author's content may be, for example, user purchase author's content, and the transaction value attribute may be, for example, a transaction price at which the user purchases the author's content. In the directed graph, if the same user purchases the contents of different creators, the creators can be connected by two by a connecting line; the line between the two authors, the arrow points to the party that has higher trading value attributes that the user has generated in favor of the content of the different authors. If the trading value attribute is the user's deal price for purchasing the originator's content, the arrow points to the party with the higher deal price for the user to purchase the content. For example, if the average deal price for user x to purchase the author Z1's content is 500 yen, the average deal price for user x to purchase the author Z2's content is 300 yen, and the average deal price for user x to purchase the author Z3's content is 400 yen, then: adding a connecting line between the author Z1 and the author Z2, wherein the arrow of the connecting line is from the author Z2 to the author Z1; adding a connecting line between the author Z1 and the author Z3, wherein the arrow of the connecting line is from the author Z3 to the author Z1; a line is added between author Z2 and author Z3, the arrow of which is from author Z2 to author Z3. The average deal price for user y to purchase the author Z1's content is 400 yen, and the average deal price for user y to purchase the author Z2's content is 450 yen, then: a further connection is added between author Z1 and author Z2, the arrow of which is from author Z1 to author Z2. In this way, a directed graph between multiple authors is constructed based on the transactional value attributes that are generated by multiple identical users supporting the content of different authors.

Through purchase behavior data of a large number of users, a directed graph among a plurality of creators can be obtained. With a directed graph between multiple creators, the weights of multiple creators can be easily calculated. For example, the weights of multiple authors are computed using a web page ranking algorithm based on a directed graph between the authors. For example, PR algorithm is used to calculate weights of multiple authors, and PR (pagerank) algorithm is a web page ranking algorithm, which is used in the present embodiment to calculate weights of the authors. The basic idea of the PR algorithm is to construct a directed graph between web pages by link relationships between the web pages, the importance of a web page being related to the number of pages linked to it. In the embodiment of the specification, the digraph of the creator is constructed, and the importance degree (weight) of the creator is related to the number of creators pointing to the creators.

Referring to FIG. 3, a directed graph is illustrated that includes 6 author nodes, respectively author P₁、P₂、P₃、P₄、P₅、P₆. The process of calculating the weight of the author using the PR algorithm may be:

PR(p_i): author p_iThe initial value of the PageRank is set manually or is a default value.

PR(p_j): author p_jThe PageRank value of (A), the initial value is set manually orThis is the default value.

Wherein i is more than or equal to 1 and less than or equal to N, j is more than or equal to 1 and less than or equal to N, i and j are integers, and i is not equal to j. In one specific example, the initial value of the PageRank value for each author may be set to 1.

N: the number of nodes in the directed graph, i.e., the number of authors in the directed graph. In the embodiment shown in fig. 3, there are 6 creators and N is 6.

M (i): to the author p_iThe author of (1).

L (j) creator p_jNumber of other creators.

The embodiment shown in fig. 3 is used for illustration: to the author P₅The author of (2) includes author P₄And author P₂I.e. to the author P₅Including author P in the author's collection₄And author P₂Wherein, the creator P₄Number of points to other creators is 3 (including 3 creators P)₅、P₃、P₁) Author P₂Number of points to other creators is 1 (including 1 creator P)₅)。

d: the value range of the damping coefficient is that d is more than 0 and less than or equal to 1. D may be determined empirically or experimentally simulated, and in one specific example is 0.85.

The PageRank values of all authors in the directed graph may be represented by the eigenvector R of the adjacency matrix,

and iterating the characteristic vector R by iterative recursion methods such as a power iteration method and the like until the characteristic vector R is converged to obtain the final PageRank value of each creator. And determining the weight of the author according to the final PageRank value of the author, wherein the larger the PageRank value of the author is, the larger the weight of the author is, and the smaller the PageRank value of the author is, the smaller the weight of the author is.

S3064, adjusting the value of the content to be evaluated according to the weight of the creator of the content to be evaluated.

In one embodiment, the value of the content to be evaluated is calculated according to the formula V2-V1 × b, where V1 is the value of the content to be evaluated before adjustment, b is the weight of the creator of the content to be evaluated, and V2 is the value of the content to be evaluated after adjustment.

For an intangible product such as content, who the creator is may have a key influence on the value of the content, and the creator weight is introduced in the embodiment to evaluate the value of the content, so that the method is more accurate and reliable.

And S308, resetting the value attribute for the content to be evaluated according to the value of the content to be evaluated.

In one embodiment, the value attribute of the content to be evaluated is set according to the value of the content to be evaluated, that is, the value of the content to be evaluated is directly used as the value attribute of the content to be evaluated.

In another embodiment, the value of the content to be evaluated is provided to the creator of the content to be evaluated, and the creator may select a coefficient within a certain range (e.g., within a range of 90% to 120%), and multiply the value by the selected coefficient, and the result is used as the value attribute of the content to be evaluated.

S310, storing the value attribute of the content to be evaluated in the block chain evidence storage platform.

And S312, showing the value attribute of the content to be evaluated to the user terminal along with the content to be evaluated.

The method for evaluating the content value attribute in the embodiment of the specification can reasonably evaluate the content to be evaluated by integrating the transaction value attribute of the content to be evaluated and the actual transaction value attribute of the similar content, can well reflect the real value of the content to be evaluated, and promotes the benign development of the platform creation ecology.

The method for evaluating the content value attribute in the embodiment of the specification is realized through a computer program, does not need manual intervention, is low in cost, is rapid and efficient, and is suitable for large-scale popularization and application.

The copyright transaction platform in the embodiment of the description can issue and store information such as content, content creator, uploading time of uploaded content of the creator and the like in the blockchain evidence storage platform, so as to prove the identity of the creator of the content. The copyright trading platform in the embodiment of the description can store and release contents such as value attributes, trading value attributes and trading behavior data in the block chain evidence storage platform. Transaction data such as purchasers, purchased contents, transaction prices, transaction time and the like can also be stored and issued on the blockchain evidence storing platform, so that the record and the evidence storing of the transfer process of the copyright are realized.

The copyright transaction platform in the embodiment of the description utilizes the block chain storage technology, so that copyright related data cannot be falsified and repudiated once generated, and the content security and the platform reliability are improved.

< evaluation device of content value Attribute >

Referring to fig. 4, the present specification further provides an estimation apparatus 10 for content value attributes, including the following modules:

a user preference data obtaining module 11, configured to obtain user preference data regarding a plurality of contents, including a content to be evaluated.

The similar content determining module 12 is configured to screen out content similar to the content to be evaluated from the plurality of contents according to the user preference data regarding the plurality of contents.

And the value estimation module 13 is configured to estimate the value of the content to be evaluated based on the transaction value attribute of the content to be evaluated and the transaction value attribute of the content similar to the content to be evaluated.

And the value attribute resetting module 14 is used for resetting the value attribute for the content to be evaluated according to the value of the content to be evaluated.

The first storage module 15 is configured to store the value attribute of the content to be evaluated in the blockchain evidence storing platform.

And the display module 16 is used for displaying the value attribute of the content to be evaluated to the user terminal along with the content to be evaluated.

Optionally, the device 10 for estimating a value attribute of content further comprises a second storage module for storing information of content and a creator of the content in the blockchain credentialing platform.

Optionally, the user preference data comprises the following types of data: data of contents displayed to a user, data of contents clicked by the user, data of contents collected by the user, data of contents purchased by the user and data of contents evaluated by the user; different types of user preference data have respective weights when content similar to the content to be evaluated is screened out from the plurality of contents.

Optionally, the step of screening out content similar to the content to be evaluated from the plurality of contents according to the user preference data on the plurality of contents comprises: and calculating the similarity of the user preference of the content to be evaluated and other contents according to the user preference data of the plurality of contents, and determining the content with the similarity of the user preference of the content to be evaluated within a preset threshold as the content similar to the content to be evaluated.

Referring to fig. 5, the present specification further provides an estimation apparatus 20 for content value attributes, which includes the following modules:

The creator weight determination module 21 is configured to calculate weights of different creators based on transaction value attributes generated by the same user supporting contents of different creators.

And the value adjusting module 22 is configured to adjust the value of the content to be evaluated according to the weight of the creator of the content to be evaluated.

And the value attribute resetting module 14 is used for resetting the value attribute of the content to be evaluated according to the adjusted value of the content to be evaluated.

Optionally, the device for estimating value attributes of content 20 further comprises a second storage module for storing the content and information of the creator of the content in the blockchain credentialing platform.

Optionally, calculating weights for different authors based on value attributes resulting from the same user supporting content of different authors comprises: directed graphs between multiple authors are constructed based on transactional value attributes generated by the same user supporting the content of different authors. And calculating the weights of the plurality of creators by using a webpage ranking algorithm according to the directed graph among the plurality of creators.

Referring to fig. 6, the present embodiment further provides an estimation apparatus 30 for content value attribute, including the following modules:

A new content obtaining module 31 for obtaining new content, the new content being content without user preference data.

And a new content category configuration module 32, configured to configure a category for the new content.

And a new content value attribute determining module 33, configured to determine an initial value attribute of the new content according to the value attribute of the content of the same category.

Optionally, the device 30 for estimating a value attribute of content further comprises a second storage module for storing the content and information of the creator of the content in the blockchain credentialing platform.

Optionally, configuring a category for the new content, including: roughly classifying the new content according to the title, the label and the description information of the new content; and determining the fine classification to which the new content belongs through a clustering algorithm.

Referring to fig. 7, the present embodiment provides an evaluation apparatus 40 for content value attributes, which includes a processor 41 and a memory 42. The memory 42 stores computer readable instructions, which when executed by the processor 41, implement the method for evaluating a content value attribute disclosed in any of the foregoing embodiments.

The evaluation device for content value attributes in the embodiments of the present specification can reasonably evaluate the content to be evaluated by integrating the transaction value attributes of the content to be evaluated and the actual transaction value attributes of similar content, can well reflect the real value of the content to be evaluated, and promotes the benign development of platform creation ecology.

The content value attribute evaluation device provided by the embodiment of the specification does not need excessive manual intervention, is low in cost, is rapid and efficient, and is suitable for large-scale popularization and application.

< copyright trading platform >

The embodiment of the specification provides a copyright trading platform, which comprises a content value attribute evaluation device disclosed in any one of the embodiments.

The embodiment of the specification provides a copyright trading platform, and the copyright trading platform comprises the content value attribute evaluation device and a block chain evidence storage platform.

The copyright trading platform in the embodiment of the specification can reasonably evaluate the contents to be evaluated by integrating the trading value attribute of the contents to be evaluated and the actual trading value attribute of the similar contents, can well reflect the real value of the contents to be evaluated, and promotes the benign development of the creation ecology of the platform.

The copyright trading platform in the embodiment of the specification does not need excessive manual intervention, is low in cost, fast and efficient, and is suitable for large-scale popularization and application.

< computer-readable Medium >

The embodiment of the present specification further provides a computer readable medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for evaluating a content value attribute disclosed in any one of the foregoing embodiments is implemented.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the embodiments of the apparatus, the device and the platform, since they are basically similar to the embodiments of the method, the description is simple, and the relevant points can be referred to the partial description of the embodiments of the method.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Embodiments of the present description may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement aspects of embodiments of the specification.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

Computer program instructions for carrying out operations of embodiments of the present description may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including AN object oriented programming language such as Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.

Aspects of embodiments of the present specification are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present description. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.

The foregoing description of the embodiments of the present specification has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for evaluating a content value attribute, comprising the steps of:

2. The method of claim 1, further comprising:

3. The method of claim 2, calculating weights for different authors based on trading value attributes of content that the same user supports the different authors, comprising:

4. The method of claim 1, the user preference data comprising the following types of data:

5. The method of claim 4, wherein the weighting of different types of user preference data is determined based on the degree of contribution of the types of user preference data to purchasing behavior.

6. The method of claim 1, wherein screening out content from the plurality of content that is similar to the content to be evaluated based on the user preference data for the plurality of content comprises:

7. The method of claim 6, wherein the user preference similarity of the content to be evaluated and other content is calculated according to the user preference data on the plurality of contents, and the content with the user preference similarity to the content to be evaluated within a preset threshold is determined as the content similar to the content to be evaluated, comprising:

8. The method of claim 1, further comprising:

9. The method of claim 8, configuring a category for the new content, comprising:

10. The method according to any one of claims 1-9, further comprising:

11. An apparatus for evaluating a content value attribute, comprising the following modules:

12. The apparatus of claim 11, further comprising:

13. The apparatus of claim 11, further comprising:

14. The apparatus of claim 11, further comprising:

15. An apparatus for evaluating a content value attribute, comprising a processor and a memory; the memory has stored therein computer readable instructions which, when executed by the processor, carry out the method of assessing a value attribute of content as claimed in any one of claims 1 to 10.

16. A copyright trading platform comprising the apparatus for evaluating a value attribute of content of any one of claims 11-15 and a blockchain credentialing platform.

17. A computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor, implement the method of assessing a value attribute of content of any one of claims 1 to 10.