Collaborative filtering recommendation method and system based on knowledge graph

Abstract

The invention provides a collaborative filtering recommendation method based on a knowledge graph. According to the method, a collaborative knowledge graph is obtained according to an entity interaction graph and a knowledge graph corresponding to an entity; inputting description information related to the entity into an entity description encoder to obtain an entity description vector; the entity description vector and the entity structure vector are mapped to the same semantic space through limitation of a mixed item based on a TransR algorithm, and the semantic space further comprises a relation vector; obtaining a self-center network vector of the entity according to the collaborative knowledge graph; obtaining an entity representation vector according to the entity description vector and the entity structure vector, wherein the entity representation vector is updated according to the entity representation vector and a self-center network vector; and obtaining an interaction probability between the user and the article according to the entity representation vector. According to the method, the problem of data sparseness in collaborative filtering is solved, the preference of the user and the characteristics of the article can be accurately represented, and the performance of the recommendation method is further improved.

Description

technical field [0001] The present invention relates to the field of big data technology, in particular to a collaborative filtering recommendation method based on a knowledge map, a collaborative filtering recommendation system based on a knowledge map, a computer-readable storage medium, and a computer device. Background technique [0002] With the continuous development and popularization of Internet technology and the advent of the era of big data, a large number of alternative information resources are generated on the Internet at all times. Users can obtain a large amount of information through various channels, such as different types of data such as text, pictures, music, and video, so as to obtain information that meets their needs. These massive amounts of information bring infinite convenience to people, but they cannot quickly obtain the information they want from these large amounts of information, making it very difficult to obtain valuable information, so how ...

AI Technical Summary

Problems solved by technology

However, with the rapid increase of the amount of data, the problem of data sparsity is gradually revealed, and these problems make the recommendation performance of the collaborative filtering method decline.

Existing solutions can only be based on content-aware recommendations and knowledge graph-based recommendations, which cannot solve the problem of data sparsity well

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