CN113095884A - Television member user recommendation method and system based on user feedback - Google Patents
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Abstract
The invention belongs to the field of signal processing, and provides a television member user recommendation method and system based on user feedback. The method comprises the steps of obtaining payment behavior information in a historical set time period, marking users who have payment behaviors in the historical set time period as seed users, and marking users who have not payment behaviors as candidate users; extracting and depicting user interest according to video playing behaviors of a user to obtain a user interest expression vector, splicing the user interest expression vector with a user portrait, and obtaining a user embedded expression vector through a full connection layer; clustering the seed users to obtain a plurality of seed user groups, representing the average vector of each seed user group as a corresponding user group vector, calculating the similarity between the candidate users and the seed user groups, and selecting the users with the similarity exceeding a set threshold value from the candidate users as the expansion users and recommending the members.
Description
Technical Field
The invention belongs to the field of signal processing, and particularly relates to a television member user recommendation method and system based on user feedback.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In the current large video networks and platforms, such as network platforms like Youku and Aiqiyi, there are abundant video resources including TV plays, movies, fantasy, etc., and these videos are provided for the users of the platforms to watch. The website platform launches member package service aiming at the videos, the package contains a plurality of videos, and the videos can be watched after the user pays for purchasing the package. Moreover, the income of the member package accounts for a large proportion of the income of the current video website, so that the platform profit can be increased and the user experience can be improved by finding users who may purchase the member package from non-member users.
The inventor finds that behavior feedback such as subsequent playing history is still related to member recommendation tasks after a user purchases a package in a television member user expansion/recommendation scene, however, existing television member user potential prediction or recommendation does not consider television member user behavior feedback information, and therefore the precision of a television member user potential prediction result and the accuracy of a recommendation user becoming a television member are reduced.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a television member user recommendation method and system based on user feedback, which can accurately depict the representation of the part of users by continuously recording the subsequent watching operation information of the users after the users purchase member packages, so as to improve the performance of the member user expansion method.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a television member user recommendation method based on user feedback.
A television member user recommendation method based on user feedback comprises the following steps:
acquiring payment behavior information in a historical set time period, marking users who have payment behaviors in the historical set time period as seed users, and marking users who have not payment behaviors as candidate users;
extracting and depicting user interest according to video playing behaviors of a user to obtain a user interest expression vector, splicing the user interest expression vector with a user portrait, and obtaining a user embedded expression vector through a full connection layer;
clustering the seed users to obtain a plurality of seed user groups, representing the average vector of each seed user group as a corresponding user group vector, calculating the similarity between the candidate users and the seed user groups, and selecting the users with the similarity exceeding a set threshold value from the candidate users as the expansion users and recommending the members.
As an embodiment, for the seed user, the process of representing the user interest representation vector is as follows:
acquiring a play history sequence in a time period set by a seed user;
dividing the play history sequence into a front play sequence and a rear play sequence according to the payment time of the seed user for placing the order;
respectively extracting an extensive film watching interest vector before a user purchases a member package and a film watching interest vector related to the package after the user purchases the member package from the front playing sequence and the rear playing sequence;
and carrying out interest enhancement extraction on the wide viewing interests before purchase by using the viewing interests related to the package to obtain the final interest expression vector of the user.
As one embodiment, a bidirectional GRU module is used for pre-play sequences to extract a broad viewing interest vector before a user purchases a member package.
As one embodiment, the self-attention module is used for the post-play sequence to extract the viewing interest vector associated with the package after the user purchases the member package.
As one embodiment, for the candidate seed user, the user interest representation vector is a user history broad viewing interest representation vector.
As one implementation mode, the expression process of the user history wide viewing interest expression vector is as follows:
acquiring a play history sequence in a time period set by a candidate seed user;
and extracting historical extensive viewing interests of the user from the pre-playing sequence to obtain a historical extensive viewing interest expression vector of the user.
As one embodiment, a bidirectional GRU module is used to extract historical broad viewing interests of the user for pre-play sequences.
A second aspect of the invention provides a television member user recommendation system based on user feedback.
A television member user recommendation system based on user feedback, comprising:
the system comprises a user dividing module, a data processing module and a data processing module, wherein the user dividing module is used for acquiring payment behavior information in a historical set time period, marking users who have payment behaviors in the historical set time period as seed users and marking users who have not payment behaviors as candidate users;
the user vector representation module is used for extracting and depicting user interest according to video playing behaviors of a user to obtain a user interest representation vector, then splicing the user interest representation vector with a user portrait, and obtaining a user embedded representation vector through a full connection layer;
and the user expansion recommendation module is used for clustering the seed users to obtain a plurality of seed user groups, the average vector of each seed user group is used as the corresponding user group vector to represent, the similarity between the candidate users and the seed user groups is calculated, and the users with the similarity exceeding a set threshold value are selected from the candidate users to be used as expansion users and member recommendation is carried out.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps in the method for television member user recommendation based on user feedback as described above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the steps in the user feedback based television member user recommendation method as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a television member user expansion method based on user feedback, which continuously records the subsequent watching operation after a user purchases a member package, and uses the information to further accurately depict the representation of the part of the user, thereby more accurately representing the interest representation of the user to the package and improving the performance of the member user expansion method.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a model framework diagram of a television member user recommendation method based on user feedback according to an embodiment of the present invention;
FIG. 2 is a flow chart of a television member user recommendation method based on user feedback according to an embodiment of the present invention;
fig. 3 is a user division processing diagram of the embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Referring to fig. 1 and fig. 2, a television member user recommendation method based on user feedback according to this embodiment specifically includes the following steps:
step 1: and acquiring the information of the payment behaviors in the historical set time period, marking the users with the payment behaviors in the historical set time period as seed users, and marking the users without the payment behaviors as candidate users.
For ease of description, a week is used for the scale. And acquiring the purchasing data information of the member package in the past one week to obtain a user id list of successfully purchasing the package and the corresponding ordering time.
And marking the users who purchase the member packages as seed users, and marking the users who do not purchase the member packages as candidate users.
The users who successfully purchased the package are the seed users, while the current non-member users are the candidate users, and the extended users are selected from the candidate users, as shown in fig. 3.
Step 2: and extracting and depicting user interest according to the video playing behavior of the user to obtain a user interest expression vector, splicing the user interest expression vector with the user portrait, and obtaining a user embedded expression vector through a full connection layer.
For seed user representation learning:
step 211, a play history sequence within a user history time period (for example, one month in the past) is obtained.
In the embodiment, the play history of each user in a month before the current time is selected, and the play history includes video id and play time information.
Step 212, the play history sequence is divided into a pre-play sequence and a post-play sequence according to the payment time of the seed user.
And dividing the play history record into play history sequences before and after purchase according to the order placing time of the seed user in the order information, and obtaining the embedded expression e of the corresponding video.
Step 213, extracting the wide viewing interest vector of the user before purchasing the member package by using the bidirectional GRU module aiming at the pre-playing sequence; the self-attention module is used for the post-play sequence to extract the viewing interest vectors of the user that are more relevant to the package after purchasing the member package.
For the play-before-purchase history sequence, a bidirectional GRU model is used, video embedding representation is carried out, meanwhile, the change situation of the video in the past and in the future is considered, a play-before-purchase video embedding representation matrix PS is obtained, and the GRU calculation formula is as follows:
wherein r istIs a reset gate, ztIt is the update of the door that is,
is a candidate activation function, htIs an activation function, etIs the input embedding vector at the t-th position in the sequence, and σ is the activation function. Obtaining a bi-directional representation
And PS ═ h1，h2，...]。
For the post-purchase play history sequence, extracting the interest distribution of the user after purchasing the set of videos by using a self-attention model to obtain a post-purchase video embedded representation AS, wherein the corresponding calculation formula is AS follows:
Q＝WQE，K＝WKE，V＝WVE
AS＝avgpoolng(Att)
wherein E is a post-playback sequence embedding matrix, W is a parameter matrix, dKFor the dimension size of K, avgpoling () is an average pooling operation.
And 214, performing interest enhancement extraction on the obtained film watching interests related to the package to the wide film watching interests before purchase to obtain the final interest expression vector of the user.
Embedding and representing PS and AS based on the obtained before and after purchase, using an activation unit to carry out interest enhancement representation on the PS by the AS, correcting the interest distribution before purchase to obtain the real interest vector IP of the user for the package, wherein the corresponding calculation formula is AS follows:
step 215, splicing the user interest expression vector and the user portrait, and obtaining a final user embedded expression vector UI through a full connection layer.
UI＝[IP，UP]
In this case, UP represents a user image.
And calculating a loss function by using the watching conditions of the videos which are currently hot played in the user playing history. The label of the watched popular film is 1, otherwise, the label is 0, and the loss is calculated as follows:
where X represents the set of hotspots we have selected, yiE {0, 1} is the corresponding label.
For candidate user representation learning, the partial computation manner is the same as the seed user representation corresponding partial learning computation manner.
Step 221, obtaining the play history sequence in the user history time period (for example, the past month). The calculation method is the same as above.
Step 222, using the bidirectional GRU module for the pre-play sequence to extract the historical broad viewing interest PS of the user. The calculation method is the same as above.
And 223, splicing the user historical wide viewing interest expression vector with the user portrait, and obtaining a final user embedded expression vector UI through a full connection layer: UI ═ PS, UP ].
And step 3: clustering the seed users to obtain a plurality of seed user groups, representing the average vector of each seed user group as a corresponding user group vector, calculating the similarity between the candidate users and the seed user groups, and selecting the users with the similarity exceeding a set threshold value from the candidate users as the expansion users and recommending the members.
In specific implementation, for seed users, a K-means method is used for clustering seed user groups to obtain K users [ G ] of different interest groups1，G2，...，GK]。
[G1，G2，..，GK]＝K-means(Ul1，UI2，..，UIn)
Wherein K is a hyper-parameter, n is the number of seed users, and G represents a seed user group.
For each user population, their average vector is calculated as the user population vector representation GE.
GE＝avgpooling(UI)，for UIinG
For each candidate user, calculating the similarity with the user group vector as the attribution score scorei of the user and each seed user group.
scorei＝cosine(UIc，GEi)
Therein, UIcEmbedded representation vectors representing candidate users, GEiA population vector representation representing a seed user population i.
The group with the highest score is selected as the user's home group, and the score is the user's expansion score.
And selecting the user with the score higher than the set threshold value as an expansion user.
And (3) calculating a loss function, namely calculating model loss by adopting a binary cross entropy loss function, wherein the calculation mode is as follows:
wherein D represents a training set, x and y respectively represent a user interest embedding vector and a corresponding label value, namely whether a user is an extended user, and p (x) is a user extension part to predict whether the user is the extended user, namely whether the user will purchase a package in the future week.
Through the analysis of the actual scene user data, when a user purchases a package, the playing proportion of the user to the video in the package is increased greatly, the average playing proportion of the video in the package is improved by about 20% before and after the package is purchased by the part of the users who purchase the package, and the amount of the users with high content is obviously increased. According to the television member user recommendation method based on user feedback, after a user purchases a member package, the user continuously records the subsequent watching operation, and the information is used for further and accurately depicting the representation of the part of the user, so that the interest representation of the user on the package is more accurately represented, and the performance of the member user expansion method is improved.
Example two
The embodiment provides a television member user recommendation system based on user feedback, which specifically comprises the following modules:
the system comprises a user dividing module, a data processing module and a data processing module, wherein the user dividing module is used for acquiring payment behavior information in a historical set time period, marking users who have payment behaviors in the historical set time period as seed users and marking users who have not payment behaviors as candidate users;
the user vector representation module is used for extracting and depicting user interest according to video playing behaviors of a user to obtain a user interest representation vector, then splicing the user interest representation vector with a user portrait, and obtaining a user embedded representation vector through a full connection layer;
and the user expansion recommendation module is used for clustering the seed users to obtain a plurality of seed user groups, the average vector of each seed user group is used as the corresponding user group vector to represent, the similarity between the candidate users and the seed user groups is calculated, and the users with the similarity exceeding a set threshold value are selected from the candidate users to be used as expansion users and member recommendation is carried out.
It should be noted that, each module in the television member user recommendation system based on user feedback in the embodiment corresponds to each step in the television member user recommendation method based on user feedback in the first embodiment one by one, and the specific implementation process is the same, which is not described herein again.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the television member user recommendation method based on user feedback as described above.
Example four
The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the television member user recommendation method based on user feedback.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, 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 specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A television member user recommendation method based on user feedback is characterized by comprising the following steps:
acquiring payment behavior information in a historical set time period, marking users who have payment behaviors in the historical set time period as seed users, and marking users who have not payment behaviors as candidate users;
extracting and depicting user interest according to video playing behaviors of a user to obtain a user interest expression vector, splicing the user interest expression vector with a user portrait, and obtaining a user embedded expression vector through a full connection layer;
clustering the seed users to obtain a plurality of seed user groups, representing the average vector of each seed user group as a corresponding user group vector, calculating the similarity between the candidate users and the seed user groups, and selecting the users with the similarity exceeding a set threshold value from the candidate users as the expansion users and recommending the members.
2. The method as claimed in claim 1, wherein the representation process of the user interest representation vector for the seed user is as follows:
acquiring a play history sequence in a time period set by a seed user;
dividing the play history sequence into a front play sequence and a rear play sequence according to the payment time of the seed user for placing the order;
respectively extracting an extensive film watching interest vector before a user purchases a member package and a film watching interest vector related to the package after the user purchases the member package from the front playing sequence and the rear playing sequence;
and carrying out interest enhancement extraction on the wide viewing interests before purchase by using the viewing interests related to the package to obtain the final interest expression vector of the user.
3. The method of claim 2, wherein the bidirectional GRU module is used for pre-play sequences to extract a broad viewing interest vector from the user prior to purchasing the member package.
4. The method of claim 2, wherein a self-attention module is used for the post-play sequence to extract a viewing interest vector associated with the package after the user purchases the member package.
5. The method as claimed in claim 1, wherein the user interest expression vector is a user history broad viewing interest expression vector for the candidate seed user.
6. The method as claimed in claim 5, wherein the expression process of the expression vector of the historical broad viewing interests of the user is:
acquiring a play history sequence in a time period set by a candidate seed user;
and extracting historical extensive viewing interests of the user from the pre-playing sequence to obtain a historical extensive viewing interest expression vector of the user.
7. The method of claim 6, wherein the bidirectional GRU module is used for pre-play sequences to extract historical broad viewing interests of the user.
8. A television member user recommendation system based on user feedback, comprising:
the system comprises a user dividing module, a data processing module and a data processing module, wherein the user dividing module is used for acquiring payment behavior information in a historical set time period, marking users who have payment behaviors in the historical set time period as seed users and marking users who have not payment behaviors as candidate users;
the user vector representation module is used for extracting and depicting user interest according to video playing behaviors of a user to obtain a user interest representation vector, then splicing the user interest representation vector with a user portrait, and obtaining a user embedded representation vector through a full connection layer;
and the user expansion recommendation module is used for clustering the seed users to obtain a plurality of seed user groups, the average vector of each seed user group is used as the corresponding user group vector to represent, the similarity between the candidate users and the seed user groups is calculated, and the users with the similarity exceeding a set threshold value are selected from the candidate users to be used as expansion users and member recommendation is carried out.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for television member user recommendation based on user feedback according to any one of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for television member user recommendation based on user feedback as claimed in any one of claims 1-7.
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| GUORUI ZHOU .ETC: "Deep Interest Evolution Network for Click-Through Rate Prediction", 《ASSOCIATION FOR THE ADVANCEMENT OF ARTIFICIAL》 * |
| LONG GUO .ETC: "Buying or Browsing? : Predicting Real-time Purchasing Intent using Att ention-based Deep Network with Multiple Behavior", 《KDD ’19》 * |
| XIAOKANG XU .ETC: "AE-UPCP: Seeking Potential Membership Users by Audience Expansion Combining User Preference with Consumption Pattern", 《DATABASE SYSTEMS FOR ADVANCED APPLICATIONS 2021》 * |
| XINYU WEN .ETC: "MISS: A Multi-user Identification Network for Shared-Account Session-Aware Recommendation", 《DATABASE SYSTEMS FOR ADVANCED APPLICATIONS 2021》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116934389A (en) * | 2023-06-12 | 2023-10-24 | 嘉兴华数广电网络有限公司 | Digital television value ticket card management system based on cloud computing and cross-platform technology |
| CN116934389B (en) * | 2023-06-12 | 2024-02-23 | 嘉兴华数广电网络有限公司 | Digital television value ticket card management system based on cloud computing and cross-platform technology |
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| CN113095884B (en) | 2022-12-06 |
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