Social Recommendation Algorithm Based on Multi-Graph Neural Network with Neighborhood Sampling
Social recommendation algorithms based on graph neural networks can obtain deep data information from graph networks to improve recommendation performance.However,with the increasing complexity of graph net-works,especially for the multi-graph neural network,node feature information learning directly affects the final recom-mendation quality.In order to improve the quality of node feature acquisition in multi-graph neural networks,a social recommendation model based on a multi-graph neural network with neighborhood sampling(MGNN-NS)is proposed.From the perspective of users and items,the proposed model samples and aggregates the neighborhood nodes of those nodes in the user-item rating graph and user social relation graph,and learns the characteristics of target nodes.This model also applies multi-head attention networks to reduce the influence of the mean aggregator,and finally obtains the characteristic representation of users and items to calculate the prediction scores for recommendation.Experiment results on the real-world datasets of Epinions and Ciao show that this model has better recommendation effect than the benchmark algorithms.