Social Recommendation Model Based on Self-Supervised Tri-Training and Consistent Neighbor Aggregation
LIU Shudong1,2, LI Liying1,2, CHEN Xu1,2
1. Centre for Artificial Intelligence and Applied Research, Zhongnan University of Economics and Law, Wuhan 430073; 2. School of Information and Engineering, Zhongnan University of Economics and Law, Wuhan 430073
Abstract Integrating user social relationships into user-item rating data to construct a heterogeneous user-item graph can alleviate data sparsity and cold start in traditional recommender systems. However, due to the complexity of user social relationships, aggregating inconsistent neighbors may degrade the recommendation performance. To address this issue, a social recommendation model based on self-supervised tri-training and consistent neighbor aggregation(SR-STCNA) is proposed. Firstly, on the basis of user-item rating data, social relationships among users are introduced and diverse relations within the heterogeneous user-item graph are established. The relationships between users as well as between users and items are presented by a hypergraph. Self-supervised tri-training is employed to learn users' representations from unlabeled data and uncover the complex connectivity between user-user and user-item interactions. Then, the consistent neighbors of users and items are aggregated in the process of their representation learning by the node consistency score and relationship self-attention on the user-item heterogeneous graph. Consequently, the representation ability of users and items is enhanced, thereby improving the recommendation performance. Finally, the experimental results on CiaoDVD, FilmTrust, Last.fm and Yelp datasets validate the superiority of SR-STCNA.
Fund:National Natural Science Foundation of China(No.61602518,72374219), General Project of National Social Science Fund of China(No.21BXW076), Project of Innovation and Talent Base for Digital Technology and Finance(No.B21038)
Corresponding Authors:
LIU Shudong, Ph.D., associate professor. His research interests include intelligent retrieval and reco-mmendation.
About author:: LI Liying, Master student. Her research interests include data mining and recommender systems.CHEN Xu, Ph.D., lecturer. His research interests include machine learning and natural language processing.
LIU Shudong,LI Liying,CHEN Xu. Social Recommendation Model Based on Self-Supervised Tri-Training and Consistent Neighbor Aggregation[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(3): 207-220.
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2024, Vol. 37 
