Abstract With the rapid development of online social platforms, social recommendation becomes a critical task in recommender systems. However, the performance of recommendation systems is limited to some extent due to the sparsity of social relationships between users. Therefore, a graph neural network recommendation method based on enhanced social influence is proposed in the paper, aiming to utilize implicit social relationships between users to enhance social recommendation. The implicit social relationships are revealed, and the social graph among users is reconstructed by analyzing interaction information between users and items. On this basis, global features of the social graph are integrated with local features of users effectively via the mutual information maximization method. A learnable mechanism is integrated into the graph attention network to fully capture the interaction information between users and items. An improved Bayesian personalized ranking loss is designed to provide more accurate user and item feature representations for the rating prediction task. Extensive experiments on three public social recommendation datasets demonstrate the superiority of the proposed method.
Fund:National Natural Science Foundation of China(No.62176244)
Corresponding Authors:
CAO Feilong, Ph.D, professor. His research interests include deep learning, neural network and its applications.
About author:: DAI Xingyue, Master student. Her research interests include deep learning and recommendation system.YE Hailiang, Ph.D, lecturer. His research interests include deep learning and graph neural network.
DAI Xingyue,YE Hailiang,CAO Feilong. Graph Neural Network Recommendation Based on Enhanced Social Influence[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(3): 221-230.
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2024, Vol. 37 
