Abstract: Graph neural network can effectively capture the complex interaction behavior between user and item in the recommendation scene. By capturing the higher-order information of nodes in the graph, recommendation performance can be improved. A Graph Attention Network with Enhanced preference influence for recommendation (GEPR) is proposed. The algorithm uses graph attention network to fuse preference influence, and then captures the potential information between user and item interaction. Specifically, the user-item bipartite graph is firstly constructed based on user-item interaction, and the attention neighborhood aggregation strategy is designed to learn the embedded representation of user and item adaptively on the graph structure. Secondly, a preference influence enhancement layer is designed to strengthen the influence of similar users(items)' preferences on target users(items)' preferences. Finally, multi-layer perceptron is used to obtain the probability score of user-item interaction by coupling the preference influence of similar users (items) on target users (items) with the embedding effect of target users (items). Experimental results on two real data sets verify the rationality and validity of the attention neighborhood aggregation strategy and preference influence in the proposed method.

Key words: enhanced preference;graph attention network;recommendation , system;preference influence