Research on Social Recommendation Algorithm Based on Multiple Attention Mechanism
Graph neural network is a deep neural network model emerged in recent years for learning graph-structured data,and has been widely applied to recommender system study due to its excellent feature learning ability.In this paper,we propose a graph-neural-network-based social recommendation algorithm with multiple attention mechanism,which embeds the user,item and user social information into the same heterogeneous graph,and employs the multiple attention network to learn the feature fusion weights of information of each part.In the information aggregation part,an attention mechanism is used to get the importance weights of different neighbor nodes,and the initial features of neighbor nodes are aggregated.The attention mechanism is also introduced in fusing the neighbor node features with its own features to obtain the final features of the node.After obtaining the final features,the attention mechanism is introduced to obtain the weights of the item and user features in the rating prediction stage.The introduction of multiple attention networks can effectively distinguish the difference in importance between different neighbors,thus obtaining more accurate feature fusion.Experiment is conducted on the Epinions and Filmtrust datasets,and the results show that the proposed method in this paper outperforms several other advantageous recommendation algorithms.
万方数据
维普
