Graph neural network recommendation method and system fusing social relation and semantic relation

Abstract

The invention belongs to the technical field of graph neural network recommendation, and discloses a graph neural network recommendation method and system fusing a social relation and a semantic relation, which utilize a simple graph neural network recommendation model to recommend a graph neural network based on user social information, score interaction information of users and items and label information of the items. Constructing the information of the non-graph structure into graph information; clustering the labels of the items by using the label information of the items; and calculating the relevancy between the items and the label clusters, carrying out cosine similarity calculation by utilizing an item-label cluster relevancy matrix, and when the similarity is greater than a certain threshold value, adding a trust relationship between the two items to form a semantic relationship graph structure of the items. According to the method, the user-project score bipartite graph is used as a basis, other isomorphic graphs are used as supplements, so that the purpose of using more neighbor nodes for aggregation is achieved, the problem of data sparsity is solved, and tests prove that the proposed model has higher recommendation accuracy than GrpahRec.

Description

technical field

[0001] The invention belongs to the technical field of graph neural network recommendation, in particular to a graph neural network recommendation method and system that integrates social relationships and semantic relationships. Background technique

[0002] At present, the most traditional recommendation algorithm is a collaborative filtering algorithm that recommends by calculating the neighbors of users or items. Because the original algorithm has limitations, many researchers have been attracted to improve it. In order to solve the problem of data sparsity and cold start, with the research of context information, the traditional recommendation algorithm using context information is to use context information as a supplement when calculating similarity. For example, the existing literature uses label information as a supplement for collaborative The similarity calculation in filtering can solve the problem of inability to make recommendations due to lack ...

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