WO2020029412A1

WO2020029412A1 - Tag recommendation method and apparatus, computer device, and computer-readable storage medium

Info

Publication number: WO2020029412A1
Application number: PCT/CN2018/108915
Authority: WO
Inventors: 吴壮伟
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-08-09
Filing date: 2018-09-30
Publication date: 2020-02-13
Also published as: CN109165975A; CN109165975B

Abstract

Embodiments of the present application provide a tag recommendation method and apparatus, a computer device, and a computer-readable storage medium. According to the embodiments of the present application, recommending tags having not yet been used by a target user to the target user on the basis of tags used by a similar user group of the target user implements not only the recommendation of tags complying with personal preferences of the target user by using common tag preferences of the similar user group, but also the unification of tags used by the similar user group, thereby avoiding excessive dispersion of tags used by users. In addition, unified tag data is conducive to subsequent analysis of common preferences of users and to other personalized marketing and promotion planning for the users.

Description

Label recommendation method, device, computer equipment and computer-readable storage medium

This application claims the priority of a Chinese patent application filed on August 9, 2018 with the Chinese Patent Office, application number 201810902677.2, and application name "Label Recommendation Method, Device, Computer Equipment, and Storage Medium", the entire contents of which are incorporated by reference. In this application.

Technical field

The present application relates to the field of Internet technologies, and in particular, to a tag recommendation method, device, computer device, and computer-readable storage medium.

Background technique

With the rapid development of e-commerce, recommendation systems have been widely studied and applied. Recommendation systems obtain user preferences by extracting and analyzing user information and behavior information. Tags are a type of data that identifies resources or users in the current era of e-commerce networks. The user's tag data can be used to analyze the user's interest preferences to help e-commerce find products that specific users recommend for their purchase. The tag data is generally provided by the e-commerce platform or social platform for users to choose and use. The number and category are fixed and may not meet the user's situation. When the tags provided by the e-commerce platform do not have tags that fit the user's preferences, they are generally Custom tags. Users with the same preferences may have different custom tags for things of the same nature. The more users, the more messy the custom tags are, resulting in diverse and difficult to unify tags, which is not good for e-commerce or social platforms. Subsequent use of tag data to analyze user preferences.

Summary of the invention

The embodiments of the present application provide a label recommendation method, device, computer device, and computer-readable storage medium, which are intended to recommend unified labels to users to avoid the situation where the labels used by users are too scattered.

In a first aspect, an embodiment of the present application provides a tag recommendation method. The method includes: obtaining a user-item rating matrix, where the user-item rating matrix includes all users and all users ’ratings of all products, and All users include the target user and several other users; calculate the similarity between each other user and the target user according to the user-item rating matrix to obtain a similar user group of the target user; Used first tags; categorizing the first tags to obtain the clusters to which each of the first tags belong; analyzing the first tags in each cluster to be used by users in the similar user group Use case; recommend the tag in the corresponding class cluster to the target user according to the situation where the first tag in each class cluster is used by the similar user group.

In a second aspect, an embodiment of the present application further provides a label recommendation device, where the label recommendation device includes a unit for implementing the label recommendation method described in the first aspect.

In a third aspect, an embodiment of the present application further provides a computer device including a memory and a processor connected to the memory; the memory is used to store a computer program that implements a tag recommendation method; and the processor is configured to run an The computer program stored in the memory is described in the method described in the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the storage medium stores one or more computer programs, and the one or more computer programs can be executed by one or more processors. To implement the method described in the first aspect above.

The tag recommendation method, device, computer equipment, and computer-readable storage medium provided in the embodiments of the present application recommend the unused tags to the target user based on the tag situation used by the similar user group of the target user, and not only can the similarity be used The common tag preferences of the user group recommend tags that match the personal preferences of the target user, and also realize the unification of the tags used by similar user groups, avoiding the situation where the tags used by users are too scattered, and the unified tag data is conducive to subsequent analysis of users' common preference , To carry out other personalized marketing plans for user groups.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solutions of the embodiments of the present application more clearly, the drawings used in the description of the embodiments are briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application. For ordinary technicians, other drawings can be obtained based on these drawings without paying creative work.

FIG. 1 is a schematic flowchart of a label recommendation method according to an embodiment of the present application; FIG.

FIG. 2 is a schematic diagram of a sub-flow of a label recommendation method according to an embodiment of the present application; FIG.

3 is a schematic diagram of a sub-flow of a label recommendation method according to another embodiment of the present application;

4 is a schematic diagram of a sub-flow of a label recommendation method according to another embodiment of the present application;

5 is a schematic block diagram of a label recommendation device according to an embodiment of the present application;

6 is a schematic block diagram of a subunit of a tag recommendation device according to an embodiment of the present application;

7 is a schematic block diagram of a subunit of a label recommendation device according to another embodiment of the present application;

8 is a schematic block diagram of a subunit of a tag recommendation device according to another embodiment of the present application;

FIG. 9 is a schematic block diagram of a structure of a computer device according to an embodiment of the present application.

detailed description

In the following, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

It should be understood that when used in this specification and the appended claims, the terms "including" and "comprising" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude one or The presence or addition of a number of other features, wholes, steps, operations, elements, components, and / or sets thereof.

It should also be understood that the term "and / or" used in the specification of the application and the appended claims refers to and includes any combination of one or more of the items listed in association and all possible combinations.

It should also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited to these terms, these terms are only used to distinguish these elements from each other.

FIG. 1 is a schematic flowchart of a tag recommendation method according to an embodiment of the present application. The method can be applied to a terminal. The terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, or other electronic devices with communication functions. The method includes steps S101 to S106.

S101. Acquire a user-item rating matrix, where the user-item rating matrix includes all users and the all user ratings for all products, and all users include a target user and several other users.

At present, various commodity consumption platforms record the purchase rating records of users purchasing products. These purchase rating records can be crawled through web crawler technology. Statistics of these purchase rating records can be obtained by all users on all products, that is, the user- Product rating matrix. All users refer to all users who have scored for product purchases. All products refer to all products included in the product consumption platform. The other users in step S101 above are relative to the target user, and their identities can be switched. When a product needs to be recommended to one user, that user is the target user, and the remaining users are other users.

S102. Calculate the similarity between each other user and the target user according to the user-product rating matrix to obtain a similar user group of the target user.

The embodiment of the present application is to recommend resources to target users based on the user's collaborative filtering idea. The user-based collaborative filtering idea is to use statistical techniques to find neighbors with the same preferences as the target user, that is, similar users (groups), and then according to the target user Of neighbors ’preferences generate recommendations to target users.

As shown in FIG. 2, step S102 includes steps S1021-S1023.

S1021. Calculate a target user vector corresponding to the target user and several comparison user vectors corresponding to the other users according to the user-product scoring matrix.

Assume that the user-item rating matrix is shown in Table 1 below:

Table 1:

Suppose U1 is the target user and U2-Um other users. In one embodiment, the vector dimension of the user vector corresponding to the other user is equal to the number of products. The vector value of one dimension corresponding to the product that has been scored is 1, and the vector value corresponding to the product that has not been scored is 0. The target user vector of U1 is

User vector to be compared for U2

User vector to be compared for U3

The vector values omitted by the ellipsis are all 0.

Because the vector value of the one-dimensional vector corresponding to the user's rating has not been 0, for the simplicity of the user vector, the user vector can be simplified based on all the products rated by the two users to be compared. Compared with U1, the two products that have been rated by the two are I1, I2, and I3, so the user vector can be reduced to 3 dimensions, and the target user vector of U1 is

User vector to be compared for U2

If U3 and U1 are compared, the two products they have rated are I1, I2, I3, and I4, so the user vector can be reduced to 4 dimensions.

S1022. Based on the cosine similarity, respectively calculate the similarity between each of the compared user vectors and the target user vector.

In this embodiment, the similar users of the target user are found based on the cosine similarity, that is, the similarity between the two users is calculated according to the following formula:

If

The similarity between the target user U1 and other users U2

If

The similarity between the target user U1 and other users U2

S1023: If the similarity is greater than or equal to a threshold, confirm other users corresponding to the similarity as similar users to obtain the similar user group.

Set a threshold. If the similarity between two users is greater than or equal to the threshold, the two users are similar, that is, they are similar users to each other. In the present application, the threshold value is 0.5-0.7. In one embodiment, the threshold value is selected as 0.5, 0.6, or 0.7.

A similar user group of the target user can be obtained by calculating the similarity between each other user and the target user.

S103. Obtain a first tag used by the similar user group.

Tags are used by users to categorize resources. Users can analyze the user's interest in a certain type of resources by using the tags frequently. In the embodiment of the present application, an arbitrary label used by a similar user group is referred to as a first label.

S104. Classify the first tags to obtain a class cluster to which each of the first tags belongs.

All the tags used by similar user groups are categorized, and the tags of which clusters the different first tags belong to are found. In this way, it is possible to analyze which tag clusters the similar user groups may be interested in.

It should be noted that before the classification of the first label or the recommendation method of the label, the labels that have been used on the network need to be clustered to obtain different clusters, and it is clear that the different clusters contain Which tags can then be used to classify the first tag in step S104 and find each class cluster described by the first tag; In addition, the class cluster to which the first tag belongs includes other than the first tag. Tags are tags that have not been used by similar user groups.

Clustering labels on the network includes the following processes:

(1) Use web crawler technology to crawl the original tag data on the network;

(2) dividing the original tag data into frequent tags and infrequent tags;

(3) Cluster frequent labels to obtain different clusters and the frequent labels contained in each cluster.

First, use the crawler technology to crawl the tag data used by different users on the network to obtain the original tag data. The crawled network can be set, mainly for mainstream networks, such as Sina Weibo, major e-commerce network platforms, Baidu, etc. Well-known web pages. Since users can initially use any text or phrase as the label of the product, the label is generally messy and wide-ranging. For the importance and concentration of the label, the original label needs to be divided into frequent labels and infrequent labels. Frequent tags refer to tags that have been used by multiple users (for example, more than 100 users) and have been marked on multiple products (for example, more than 100 products); infrequent tags are not It is often used by users, so it is eliminated. After clustering frequent labels, different clusters and the frequent labels contained in each cluster can be obtained. Labels are a kind of text resources. Using the existing corpus and word2vec algorithm, you can train word vectors with arbitrary labels. After you get word vectors with frequent labels, you use the DBScan model to cluster word vectors with frequent labels to get the clusters of labels.

After the class of tags is obtained, when a user's behavior generates a trigger event that triggers tag recommendation, the tag recommendation method of the present application is used to recommend tags to the user. For example, if the medical care wants to evaluate the purchased product after shopping, the evaluation process requires the user to tag the product, and the user's evaluation operation can be regarded as a trigger event.

S105. Analyze a situation in which the first tag in each cluster is used by users in the similar user group.

S106. Recommend the tags in the corresponding cluster to the target user according to the situation that the first tag of each cluster is used by the similar user group.

After classifying each first tag, analyze the use of the first tag of each type of cluster by similar user groups. Since each user in the similar user group has the same preference for the same resource, it can be based on each The overall situation where the first type of tags of a class cluster is used by similar users to predict which type of cluster tags are more interested by similar user groups, thereby predicting which type of cluster tags the target user is more interested in, and recommending them to the target user More interested tags.

In an embodiment, step S105 specifically includes: separately calculating a total frequency of the first tag in each type of cluster used by the similar user group.

The situation where the first tags of a certain type of cluster are used by similar user groups can be represented by the total frequency of all the first tags contained in them by the similar user group; all the first tags contained in a type of cluster are The total frequency used by the similar user group is calculated according to the situation where each first tag in the cluster is used by the similar user group.

Further, as shown in FIG. 3, step S105 includes steps S1051-S1052.

S1051. Calculate the frequency with which the first tag is used by the similar user group according to the similarity corresponding to each similar user and the number of times each similar user uses a first tag.

S1052. Calculate the sum of the frequencies of all the first tags in the same cluster used by the similar user group, and confirm the sum of the frequencies as the total frequency of the first tags of the corresponding cluster used by the similar user group. .

Assume that there are K first tags in this type of cluster, and the frequency of the j-th first tag used by the i-th similar user is calculated according to the formula f _ij = si * Q _ij , where f _ij indicates that the i-th similar user uses the first The frequency of the j first tags, si represents the similarity between the i-th similar user and the target user, and Q _ij represents the number of times the i-th similar user uses the j-th first tag. Using similarity as a weighted value of how frequently tags are used, the more similar between users, the closer the preferences between users are, so the higher the similarity and the higher the weight, the more reference the corresponding similar users use the tags Importantly, this is more personalized for tag recommendations.

Assuming that there are M similar users in the similar user group, the frequency (represented by F _j ) that the j-th first label is used by the similar user group is equal to the number of M similar users using the j-th first label. Sum of frequencies, ie

The sum of the frequencies of all the first tags in the same cluster used by the similar user group, that is, the total frequencies in step S1052, the calculation formula is as follows:

In an embodiment, step S106 specifically includes: recommending the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tag of each cluster.

The larger the total frequency, the more frequently the first label of the corresponding cluster is used, and the higher the probability that the labels in the cluster are used by the similar user group and the target user, the labels in the cluster are recommended to the target user , To avoid the tags that are customized by the same user group leading to too scattered tags, thereby realizing the unified use of tags by similar user groups.

Further, as shown in FIG. 4, step S106 includes steps S1061-S1064.

S1061. Obtain all tags included in a preset number of clusters with a total frequency ranking.

Sort the clusters according to the order of the total frequency from high to low to obtain TopN clusters, that is, the first preset (N) clusters with a higher total frequency, where N is 1-4. In one embodiment, the value of N is 2 or 3.

The TopN clusters are tags that are frequently used by similar user groups, and also represent the tags that are frequently used by the target user.

S1062. Obtain a tag used by the target user.

S1063. According to the tags used by the target user, obtain, from all the tags, tags that the target user has not used.

S1064. Recommend the obtained unused tags to the target user.

Get all recommended tags in the TopN clusters that have not been used by the target user, form a list of recommended tags for different clusters and feed them back to the target user, and then the user can select the tags of the corresponding cluster in different recommended tag lists .

The tag recommendation method provided in the embodiment of the present application recommends an unused tag to the target user based on the tag situation used by the similar user group of the target user, and not only can use the common tag preference of the similar user group to recommend matching the personality of the target user The preferred tags also realize the unification of the tags used by similar user groups, avoiding the situation where the tags used by users are too scattered, and the unified tag data is conducive to subsequent analysis of the user's common preferences, and other personalized marketing promotion for the user group plan.

FIG. 5 is a schematic block diagram of a label recommendation device 100 according to an embodiment of the present application. The tag recommendation device 100 includes a unit for performing the above-mentioned tag recommendation method, and the device may be configured in a desktop computer, a tablet computer, a laptop computer, and other terminals. The tag recommendation device 100 includes a first acquisition unit 101, a first calculation unit 102, a second acquisition unit 103, a classification unit 104, an analysis unit 105, and a recommendation unit 106.

The first obtaining unit 101 is configured to obtain a user-item scoring matrix, where the user-item scoring matrix includes all users and the scoring of all products by all users, and all users include a target user and several other users.

The first calculation unit 102 is configured to calculate the similarity between each other user and the target user according to the user-item scoring matrix to obtain a similar user group of the target user.

The second obtaining unit 103 is configured to obtain a first tag used by the similar user group.

The classifying unit 104 is configured to classify the first tags to obtain a class cluster to which each of the first tags belongs.

The analysis unit 105 is configured to analyze a situation in which a first tag in each cluster is used by a user in the similar user group.

The recommendation unit 106 is configured to recommend the tags in the corresponding cluster to the target user according to the situation that the first tag of each cluster is used by the similar user group.

In an embodiment, as shown in FIG. 6, the first calculation unit 102 includes the following subunits:

A first calculation subunit 1021, configured to calculate a target user vector corresponding to the target user and several comparison user vectors corresponding to the other users according to the user-product rating matrix;

A second calculation subunit 1022, configured to separately calculate the similarity between each comparison user vector and the target user vector based on the cosine similarity; and

The confirming subunit 1023 is configured to confirm other users corresponding to the similarity as similar users if the similarity is greater than or equal to a threshold, so as to obtain the similar user group.

In an embodiment, the analysis unit 105 is specifically configured to separately calculate a total frequency of the first tag in each type of cluster used by the similar user group.

The recommendation unit 106 is specifically configured to recommend the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tag of each cluster.

In an embodiment, as shown in FIG. 7, the analysis unit 105 includes:

A third calculation subunit 1051, configured to calculate, according to the similarity corresponding to each similar user and the number of times each similar user uses a first tag, the frequency with which the first tag is used by the similar user group; and

A fourth calculation subunit 1052 is configured to calculate a sum of frequencies of all the first tags in the same cluster used by the similar user group, and confirm the sum of the frequencies as the first tags of the corresponding cluster are similar to the The total frequency used by the user community.

In an embodiment, as shown in FIG. 8, the recommendation unit 106 includes:

A first obtaining subunit 1061, configured to obtain all tags included in a preset number of clusters with a total frequency ranking first;

A second acquisition subunit 1062, configured to acquire a tag used by the target user;

A third obtaining subunit 1063, configured to obtain, from all the tags, tags that have not been used by the target user according to the tags that have been used by the target user; and

The recommendation subunit 1064 is configured to recommend the obtained unused tags to the target user.

The above-mentioned label recommendation device 100 corresponds to the foregoing label recommendation method. For the details of the label recommendation device 100 in this embodiment, reference may be made to the foregoing method embodiment, and details are not described herein.

The above-mentioned tag recommendation device 100 may be implemented in the form of a computer program, and the computer program may be run on a computer device as shown in FIG. 9.

FIG. 9 is a schematic block diagram of a structure of a computer device 200 according to an embodiment of the present application. The computer device 200 may be a terminal or a server. The terminal may be an electronic device with a communication function, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device. The server can be an independent server or a server cluster consisting of multiple servers.

The computer device 200 includes a processor 202, a memory, and a network interface 205 connected through a system bus 201. The memory may include a non-volatile storage medium 203 and an internal memory 204.

The non-volatile storage medium 203 of the computer device 200 may store an operating system 2031 and a computer program 2032. When the computer program 2032 is executed, the processor 202 may execute a tag recommendation method. The internal memory 204 provides an environment for running the computer program 2032 in the non-volatile storage medium 203. The processor 202 of the computer device 200 is used to provide computing and control capabilities to support the operation of the entire computer device 200. The network interface 205 of the computer device 200 is used for network communication, such as sending assigned tasks and receiving data.

Those skilled in the art can understand that the embodiment of the computer device shown in FIG. 9 does not constitute a limitation on the specific configuration of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure. Either some parts are combined or different parts are arranged. For example, in some embodiments, the computer device may include only a memory and a processor. In such an embodiment, the structure and function of the memory and the processor are consistent with the embodiment shown in FIG. 9, and details are not described herein again.

When the processor 202 runs the computer program 2032 in the non-volatile storage medium 203, the processor 202 performs the following steps: obtaining a user-item rating matrix, where the user-item rating matrix includes all users and the all users on all products All users include a target user and several other users; calculating the similarity between each other user and the target user according to the user-item rating matrix to obtain a similar user group of the target user; obtaining the similar user group of the target user; First tags used by similar user groups; categorizing the first tags to obtain the clusters to which each first tag belongs; analyzing the first tags in each class cluster by the similar user groups The situation used by the users in the cluster; recommend the tags in the corresponding cluster to the target user according to the situation that the first tag of each cluster is used by the similar user group.

In an embodiment, when the processor 202 executes the step of calculating the similarity between each other user and the target user according to the user-item scoring matrix to obtain a similar user group of the target user, the processor 202 specifically Perform the following steps: calculate a target user vector corresponding to the target user and several comparison user vectors corresponding to the other users according to the user-item scoring matrix; and calculate each comparison user vector and the target separately based on cosine similarity Similarity of user vectors; if the similarity is greater than or equal to a threshold, other users corresponding to the similarity are confirmed as similar users to obtain the similar user group.

In an embodiment, when the processor 202 executes the step of analyzing the situation in which the first tag in each type of cluster is used by users in the similar user group, the processor 202 specifically performs the following steps: The total number of first tags in a class of clusters used by the similar user population.

In an embodiment, when the processor 202 executes the step of recommending the tags in the corresponding cluster to the target user according to the situation that the first tag of each cluster is used by the similar user group, Specifically, the following steps are performed: recommending tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tag of each cluster.

In an embodiment, when the processor 202 executes the step of separately calculating the total frequency of the first tag in each type of cluster used by the similar user group, the processor 202 specifically performs the following steps: according to each similar The user's corresponding similarity and the number of times each similar user uses a first tag to calculate the frequency with which the first tag is used by the similar user group; calculate all first tags in the same type of cluster by the similar user group The sum of the used frequencies confirms the sum as the total frequency of the first tag of the corresponding cluster used by the similar user group.

In an embodiment, when the processor 202 executes the step of recommending the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tag of each cluster, the processor 202 specifically performs the following steps: All tags included in the preset number of clusters with the total frequency ranked; Get tags used by the target user; Get the target user among all tags according to the tags used by the target user Unused tags; recommend the obtained unused tags to the target user.

It should be understood that, in the embodiment of the present application, the processor 202 may be a central processing unit (CPU), and the processor 202 may also be another general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), Application-specific integrated circuits (Application Specific Integrated Circuits, ASICs), ready-made programmable gate arrays (Field-Programmable Gate Arrays, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor.

A person of ordinary skill in the art can understand that all or part of the processes in the method of the foregoing embodiment can be implemented by using a computer program to instruct related hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, and the storage medium is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the method.

Therefore, the present application also provides a computer-readable storage medium, where the computer-readable storage medium stores one or more computer programs, and the one or more computer programs can be executed by one or more processors. The following steps are implemented: obtaining a user-item rating matrix, the user-item rating matrix including all users and the all user ratings for all products, the all users including target users and several other users; according to the user-item The scoring matrix calculates the similarity between each other user and the target user to obtain a similar user group of the target user; obtains a first tag used by the similar user group; classifies the first tag to obtain The class cluster to which each of the first tags belongs; analyzing the situation in which the first tag in each cluster is used by users in the similar user group; according to the first tag of each class cluster, the similar user is used The situation used by the group recommends the tags in the corresponding cluster to the target user.

In an embodiment, when implementing the step of calculating the similarity between each other user and the target user according to the user-item scoring matrix to obtain a similar user group of the target user, the following steps are specifically implemented: The user-item scoring matrix calculates a target user vector corresponding to the target user and several comparison user vectors corresponding to the other users; and calculates the similarity between each comparison user vector and the target user vector based on the cosine similarity. If the similarity is greater than or equal to a threshold, confirming other users corresponding to the similarity as similar users to obtain the similar user group.

In an embodiment, when implementing the step of analyzing the situation where the first tag in each type of cluster is used by users in the similar user group, the following steps are specifically implemented: calculating the The total number of first tags used by the similar user population.

In an embodiment, when implementing the step of recommending the tags in the corresponding cluster to the target user according to the situation that the first tag of each cluster is used by the similar user group, the following steps are specifically implemented: Recommending the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tag of each cluster.

In an embodiment, when the step of separately calculating the total frequency of the first tag in each type of cluster used by the similar user group is implemented, the following steps are specifically implemented: according to the similarity corresponding to each similar user And the number of times each first user uses a first tag to calculate the frequency with which the first tag is used by the similar user group; calculate the sum of the frequencies that all first tags in the same cluster are used by the similar user group , The total frequency of the first tag whose corresponding sum is confirmed as the corresponding cluster is used by the similar user group.

In an embodiment, when implementing the step of recommending the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first label of each type of cluster, the following steps are specifically implemented: obtaining the total frequency ranking in All tags included in the previous preset number of clusters; obtaining tags used by the target user; and obtaining tags not used by the target user among all tags according to the tags used by the target user ; Recommending the obtained unused tags to the target user.

The computer-readable storage medium may be a non-volatile storage medium, which is an internal storage unit of the foregoing device, such as a hard disk or a memory of the device, and the storage medium may also be an external storage device of the device, such as on the device. Equipped with plug-in hard disk, Smart Memory Card (SMC), Secure Digital (SD) card, Flash Card, U disk, mobile hard disk, Read-Only Memory, A variety of computer-readable storage media, such as ROM), magnetic disks, or optical disks, that can store program codes. Further, the computer-readable storage medium may also include both an internal storage unit of the device and an external storage device.

The above is only a specific implementation of this application, but the scope of protection of this application is not limited to this. Any person skilled in the art can easily think of various equivalents within the technical scope disclosed in this application. Modifications or replacements, and these modifications or replacements should be covered by the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

A label recommendation method includes:

Obtaining a user-item rating matrix, where the user-item rating matrix includes all users and the all user ratings for all products, and all users include a target user and several other users;

Calculating the similarity between each other user and the target user according to the user-product rating matrix to obtain a similar user group of the target user;

Acquiring a first tag used by the similar user group;

Classify the first tags to obtain a class cluster to which each of the first tags belongs;

Analyze the situation where the first label in each cluster is used by users in the similar user group;

Recommend the tags in the corresponding cluster to the target user according to the situation that the first tag of each cluster is used by the similar user group.
The tag recommendation method according to claim 1, wherein the calculating the similarity between each other user and the target user according to the user-item scoring matrix to obtain a similar user group of the target user, comprising:

Calculating a target user vector corresponding to the target user and several comparison user vectors corresponding to the other users according to the user-item scoring matrix;

Separately calculating the similarity between each comparison user vector and the target user vector based on the cosine similarity;

If the similarity is greater than or equal to a threshold, other users corresponding to the similarity are confirmed as similar users to obtain the similar user group.
The tag recommendation method according to claim 1, wherein the analyzing a situation in which a first tag in each cluster is used by a user in the similar user group comprises:

Calculate the total frequency of the first tag in each type of cluster used by the similar user group;

The recommending the tags in the corresponding cluster to the target user according to the situation that the first tags of different clusters are used by the similar user group includes:

Recommending the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tag of each cluster.
The tag recommendation method according to claim 3, wherein the calculating the total frequency of the first tag in each type of cluster used by the similar user group comprises:

Calculating the frequency with which the first tag is used by the similar user group according to the similarity corresponding to each similar user and the number of times each similar user uses a first tag;

Calculate the sum of the frequencies of all the first tags in the same cluster used by the similar user group, and confirm the sum of the frequencies as the total frequency of the first tags of the corresponding cluster used by the similar user group.
The tag recommendation method according to claim 3, wherein the recommending the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tag of each cluster includes:

Obtain all tags included in a preset number of clusters with a total frequency rank;

Acquiring tags used by the target user;

Obtaining, according to the tags used by the target user, among all tags, tags that have not been used by the target user;

The obtained unused tags are recommended to the target user.
A label recommendation device includes:

A first obtaining unit, configured to obtain a user-item scoring matrix, where the user-item scoring matrix includes all users and the scoring of all products by all users, and all users include a target user and several other users;

A first calculation unit, configured to calculate the similarity between each other user and the target user according to the user-item scoring matrix to obtain a similar user group of the target user;

A second obtaining unit, configured to obtain a first tag used by the similar user group;

A classifying unit, configured to classify the first tags to obtain a class cluster to which each of the first tags belongs;

An analysis unit, configured to analyze a situation in which a first tag in each cluster is used by a user in the similar user group;

A recommendation unit is configured to recommend the tags in the corresponding cluster to the target user according to the situation that the first tag of each cluster is used by the similar user group.
The tag recommendation device according to claim 6, wherein the first calculation unit comprises:

A first calculation subunit, configured to calculate a target user vector corresponding to the target user and several comparison user vectors corresponding to the other users according to the user-product rating matrix;

A second calculation subunit, configured to separately calculate the similarity between each comparison user vector and the target user vector based on the cosine similarity;

A confirmation subunit is configured to, if the similarity is greater than or equal to a threshold, confirm other users corresponding to the similarity as similar users to obtain the similar user group.
The tag recommendation device according to claim 6, wherein the analysis unit is specifically configured to:

Calculate the total frequency of the first tag in each type of cluster used by the similar user group;

The recommendation unit is specifically configured to recommend the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tags of the different clusters.
The tag recommendation device according to claim 8, wherein the analysis unit comprises:

A third calculation subunit, configured to calculate, according to the similarity corresponding to each similar user and the number of times each similar user uses a first tag, the frequency with which the first tag is used by the similar user group;

A fourth calculation subunit, configured to calculate a sum of frequencies of all first tags in the same cluster used by the similar user group, and confirm the sum of frequencies as the first tags of the corresponding cluster are used by the similar user The total frequency used by the population.
The tag recommendation device according to claim 8, wherein the recommendation unit comprises:

A first acquisition subunit, configured to acquire all tags included in a preset number of clusters with a total frequency ranking first;

A second acquisition subunit, configured to acquire a tag used by the target user;

A third obtaining subunit, configured to obtain, from all the tags, tags that have not been used by the target user according to the tags that have been used by the target user;

A recommendation subunit, configured to recommend the obtained unused tags to the target user.
A computer device including a memory and a processor connected to the memory;

The memory is used to store a computer program for implementing the tag recommendation method;

The processor is configured to run a computer program stored in the memory to perform the following steps: obtaining a user-item rating matrix, where the user-item rating matrix includes all users and all users' ratings for all products, the All users include a target user and several other users; calculating the similarity between each other user and the target user according to the user-item rating matrix to obtain a similar user group of the target user; obtaining the similar user group used by The first tags that have passed; classify the first tags to obtain the clusters to which each of the first tags belong; analyze the first tags in each cluster to be used by users in the similar user group According to the situation that the first label of each cluster is used by the similar user group, the target user is recommended to use the label in the corresponding cluster.
The computer device according to claim 11, wherein the processor executes the calculation of the similarity between each other user and the target user according to the user-item scoring matrix to obtain a similar user group of the target user Step, specifically performing the following steps: calculating a target user vector corresponding to the target user and several comparison user vectors corresponding to the other users according to the user-product scoring matrix; calculating each comparison user separately based on cosine similarity The similarity between the vector and the target user vector; if the similarity is greater than or equal to a threshold, other users corresponding to the similarity are confirmed as similar users to obtain the similar user group.
The computer device according to claim 11, wherein when the processor executes the step of analyzing a situation where a first tag in each type of cluster is used by a user in the similar user group, the processor specifically executes the following Step: Calculate the total frequency of the first tag in each type of cluster used by the similar user group;

When the processor executes the step of recommending the tags in the corresponding cluster to the target user according to the situation that the first tag of each cluster is used by the similar user group, the processor specifically executes the following steps: The total frequency corresponding to the first tag of a class of clusters recommends the tag in the corresponding class of clusters to the target user.
The computer device according to claim 13, wherein the processor specifically executes the following steps when performing the step of separately calculating the total number of first tags in each type of cluster used by the similar user group : Calculate the frequency with which a first tag is used by the similar user group according to the similarity corresponding to each similar user and the number of times each similar user uses a first tag; calculate all first tags in the same cluster The sum of frequencies used by the similar user group is described, and the sum is confirmed as the total number of frequencies used by the first tag of the corresponding class cluster by the similar user group.
The computer device according to claim 13, wherein when the processor executes the step of recommending the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tag of each cluster, specifically The following steps are performed: obtaining all tags included in a preset number of clusters with a total frequency ranking; obtaining tags used by the target user; and among all tags according to the tags used by the target user Acquiring the unused tags of the target user; recommending the acquired unused tags to the target user.
A computer-readable storage medium stores one or more computer programs, and the one or more computer programs can be executed by one or more processors to implement the following steps: acquiring a user -Product rating matrix, the user-product rating matrix includes all users and the all user ratings for all products, the all users include a target user and several other users; each is calculated according to the user-product rating matrix Similarity between other users and the target user to obtain a similar user group of the target user; obtain a first tag used by the similar user group; classify the first tag to obtain each of the first The cluster to which a tag belongs; analyzes the situation where the first tag in each category cluster is used by users in the similar user group; according to the situation where the first tag in each category cluster is used by the similar user group Recommend tags in the corresponding cluster to the target user.
The computer-readable storage medium according to claim 16, wherein in implementing the calculation of the similarity between each other user and the target user according to the user-item scoring matrix, to obtain a similar user group of the target user In the step, the following steps are specifically implemented: calculating a target user vector corresponding to the target user and several comparison user vectors corresponding to the other users according to the user-item scoring matrix; and calculating each comparison user vector separately based on cosine similarity Similarity with the target user vector; if the similarity is greater than or equal to a threshold, other users corresponding to the similarity are confirmed as similar users to obtain the similar user group.
The computer-readable storage medium according to claim 16, wherein when implementing the step of analyzing a situation where a first tag in each cluster is used by a user in the similar user group, the following steps are specifically implemented : Calculate the total frequency of the first tag in each type of cluster used by the similar user group separately;

When implementing the step of recommending a tag in a corresponding class cluster to the target user according to the situation that the first label of each class cluster is used by the similar user group, the following steps are specifically implemented: The total frequency corresponding to the first label recommends the label in the corresponding cluster to the target user.
The computer-readable storage medium according to claim 18, wherein when implementing the step of separately calculating the total frequency of the first tag in each type of cluster used by the similar user group, the following steps are specifically implemented: Calculate the frequency with which a first tag is used by the similar user group according to the similarity corresponding to each similar user and the number of times each similar user uses a first tag; calculate all first tags in the same cluster A sum of frequencies used by similar user groups, and the sum is confirmed as a total frequency used by the first tag of the corresponding class cluster by the similar user groups.
The computer-readable storage medium according to claim 18, wherein when implementing the step of recommending the tags in the corresponding cluster to the target user according to the total frequency corresponding to the first tag of each cluster, the specific implementation is implemented The following steps: obtaining all tags included in a preset number of clusters with a total frequency ranking; obtaining tags used by the target user; obtaining among all tags according to the tags used by the target user The unused tags of the target user; recommending the obtained unused tags to the target user.