Social recommendation method based on multi-feature heterogeneous graph neural network

Abstract

The invention discloses a social recommendation method based on a multi-feature heterogeneous graph neural network. The method comprises the following steps: extracting various attribute information of social network users and topics for coding; processing the user coding information and the topic coding information through a multi-layer perceptron to obtain initial feature vector representation of each user and topic; establishing a heterogeneous graph by taking users and topics as nodes, inputting the heterogeneous graph into a heterogeneous graph neural network, performing information transmission in the graph in combination with an attention mechanism, and updating feature vector representation; and performing similarity calculation on the user feature vectors, and selecting top-k users and top-k topics with the highest similarity with the user vectors for recommendation. Various types of attribute information of the users and the topics is extracted at the same time, the users and the topics serve as nodes at the same time to establish the heterogeneous graph, social information can be mined more comprehensively, information transmission and aggregation are carried out through the heterogeneous graph neural network, features of the users and interested topics of the users are deeply fused, and recommendation accuracy and user experience are improved.

Description

technical field [0001] The invention relates to the technical field of data mining information recommendation, in particular to a social recommendation method based on a multi-feature heterogeneous graph neural network. Background technique [0002] In recent years, with the rapid development of the Internet and information technology and its subsidiary industries, Internet services and transactions have become more and more popular, and people are more and more inclined to use the Internet for social activities. A large amount of user behavior information, such as mutual attention, comments, likes, and exchanges on topics of common interest among users, has caused information overload. Although this provides users with rich information, it inevitably makes users spend a lot of time on a large amount of information. information that they are not interested in, so information overload is both a challenge and an opportunity for users and social platforms. The emergence of the...

AI Technical Summary

Problems solved by technology

[0003] In the existing social recommendation system technology, most of them only consider some sparse information (such as interaction between users, comments, likes, etc.), and do not consider the influence of multiple factors on the recommendation system. As a result, information mining on social networks is not sufficient. At the same time, most of the current technologies only focus on user interaction or topics of interest to users, which will also lead to information loss and have certain limitations.

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