Collaborative filtering recommendation method based on enhanced graph learning

Abstract

The invention discloses a collaborative filtering recommendation method based on enhanced graph learning, and the method comprises the steps: 1, constructing a bipartite graph of a user to a product, wherein the bipartite graph comprises a user node set, a product node set and an adjacent matrix; 2, obtaining an embedded matrix as a node feature through one-hot coding; 3, calculating a similar matrix according to the current node characteristics, and performing sparsification; 4, taking the sparse similar matrix as a residual item and adding the residual item to the adjacent matrix to obtain an enhanced adjacent matrix; 5, carrying out feature propagation according to the enhanced adjacent matrix structure graph convolution layer, and obtaining node representation; and 6, obtaining a score matrix from the prediction layer according to the node representation, thereby realizing product recommendation. According to the method, the graph structure information can be adaptively learned based on the similarity between the nodes, and the robustness and integrity of the graph are enhanced, so that more accurate node representation is learned, and the recommendation performance is improved.

Description

technical field [0001] The invention relates to the field of personalized recommendation, in particular to a collaborative filtering recommendation method based on enhanced graph learning. Background technique [0002] In the Internet era of information explosion, information overload has become a problem that restricts users from effectively obtaining external information. The recommendation system aims to mine the historical behavior of users and recommend products that meet their interests and preferences to help users obtain the desired information from massive data. The recommendation model based on collaborative filtering is one of the most mainstream recommendation systems, and its modeling of users' potential interests can make personalized recommendations. Collaborative filtering models are widely used in recommendation scenarios, but their performance is limited by the sparsity of data. [0003] The graph-based collaborative filtering model models the user-produc...

AI Technical Summary

Problems solved by technology

In the recommendation system based on collaborative filtering, user nodes and product nodes lack feature information, and the traditional graph reconstruction method based on feature points is difficult to work, which brings difficulties to graph learning

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