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A radio and television program recommendation method based on knowledge graph and user micro-behavior

A knowledge graph and program recommendation technology, applied in the field of radio and television program recommendation based on knowledge graph and user micro-behavior, can solve problems such as not taking into account differences in user interests, ignoring the internal connection of content attributes, etc., to achieve strong correlation and enhance individuality effect

Active Publication Date: 2022-03-29
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The present invention ignores various behavior types of users in the prior art, does not take into account the user interest differences implied by different feedback behaviors of users, and when mining the dynamic changes of user preferences, the embedding of items obtained from the feature point of view often ignores the In order to solve problems such as the internal connection of content attributes between items, a radio and television program recommendation method based on knowledge graph and user micro-behavior is proposed. By combining item2vec with random walk, the random walk sequence obtained by the knowledge graph attribute subgraph Trained together with session-based user behavior sequences, the trained embedding vector incorporates the similarity of program content attributes and user interaction sessions

Method used

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  • A radio and television program recommendation method based on knowledge graph and user micro-behavior
  • A radio and television program recommendation method based on knowledge graph and user micro-behavior
  • A radio and television program recommendation method based on knowledge graph and user micro-behavior

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Embodiment 1

[0107] The present invention proposes a method for recommending radio and television programs based on knowledge graphs and user microbehaviors, such as figure 1 As shown, it specifically includes the following steps:

[0108] Step S1: Build a knowledge graph in the field of radio and television;

[0109] Step S2: Sort out user micro-behavior data: divide user behavior interaction data into continuous micro-behavior and discrete micro-behavior according to behavior duration;

[0110] Step S3: Extract the attribute subgraph of the broadcasting field knowledge map constructed in step S1, and use the random walk method to extract a random walk program sequence Q according to the attribute subgraph; extract a random walk from one attribute subgraph Sequence Q, the random walk sequence Q of all attribute subgraphs together form an item sequence set H k ;

[0111] Step S4: Utilize the user's behavior interaction data sorted out in step S2 to construct a time session-behavior type...

Embodiment 2

[0118] This embodiment is based on the above-mentioned embodiment 1, in order to better realize the present invention, further, as figure 2 As shown, the specific operation is:

[0119]Step S1: Build a knowledge map in the field of radio and television: Crawl the network resources of radio and television programs, form structured data through entity alignment, and use the ontology modeling tool protégé to complete the ontology construction; after the ontology construction is completed, use d2rq to store the original in the relation The data in the large-scale database is converted into the corresponding rdf format, and then stored in the form of a graph database, and then the construction of the knowledge map in the field of broadcasting and television is completed.

[0120] In order to better realize the present invention, further, when constructing the knowledge map in the field of broadcasting and television, the knowledge map is constructed in a top-down manner, and the d...

Embodiment 3

[0123] In this embodiment, on the basis of any one of the above-mentioned embodiments 1-2, in order to better realize the present invention, further, the specific operation of the step S2 is: divide the user's behavior interaction data into continuous Continuous micro-behaviors and discrete micro-behaviors; the continuous micro-behaviors are user behaviors that can last for a certain period of time, including live viewing, on-demand viewing, and search and viewing behaviors; the discrete micro-behaviors are user behaviors that only occur at a certain moment Behaviors, including purchases, favorites, and likes; collect the data detected by the background of the radio and television system to form structured data that records user numbers, media asset numbers, behavior types, behavior timestamps, and behavior durations. The behavior duration of the behavior is the corresponding valid value, and the behavior duration of the discrete behavior type is null.

[0124] Other parts of ...

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Abstract

The present invention proposes a radio and TV program recommendation method based on knowledge graph and user micro-behavior, combines item2vec with random walk, and trains the random walk sequence of the attribute subgraph with the user behavior sequence to obtain a combination of program content attributes Embedding vectors of similarity to two levels of user interaction sessions. Then, in the case of obtaining the program embedding and the type-classified micro-behavior embedding, according to the historical interaction records between the user and the program, the behavior embedding and the corresponding program embedding are spliced, and then through the semantic space network mapping, the same dimension is obtained. Behavior-program semantic representation forms an embedded sequence of user historical behavior; finally, based on the Transformer encoding-decoding mechanism, the user’s historical behavior sequence is encoded and mapped with self-attention to obtain the user’s semantic features with implicit dynamic preferences, and uses the target program’s attention Decode user semantic feature map to get user semantic preference.

Description

technical field [0001] The invention belongs to the technical field of radio and television program recommendation, and in particular relates to a radio and television program recommendation method based on knowledge graphs and user microscopic behaviors. Background technique [0002] With the integration of the three-network service of the telecommunication network, the radio and television network and the computer communication network, the services provided by the radio and television network are becoming more and more abundant and updated faster and faster. Due to the increase in the number of TV channels and the emergence of IPTV services and new media services, users exposed to TV terminals can obtain more and more TV program content. However, such an excess of TV shows also burdens TV viewers, as it takes longer to search for their favorite TV show content. The recommendation system can help users to efficiently filter out the information they are interested in, and ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/36G06F16/25G06F16/951G06F16/9535G06F40/30G06N3/04G06N3/08
CPCG06F16/367G06F16/258G06F16/951G06F16/9535G06F40/30G06N3/08G06N3/048
Inventor 詹会兰向超雷航杨茂林
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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