Social Recommendation Model Based on Self-Supervised Graph Masked Neural Networks
ZANG Xiubo1, XIA Hongbin1,2, LIU Yuan1,2
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122; 2. Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122
Abstract：The existing self-supervised social recommendation models mostly construct self-supervised signals through the strategies of manual heuristic graph enhancement and contrasts between single-relational views. Thus, the performance of the model is easily affected by the quality of enhanced self-supervised signals, making it challenging to adaptively suppress noise. To solve these problems, a social recommendation model based on self-supervised graph masked neural networks(SGMN) is proposed. Firstly, single-relational views for user-social interaction and item classification are constructed respectively, as well as high-order connected heterogeneous graphs. Specifically, the graph masked learning paradigm is adopted to guide adaptive and learnable data augmentation for the user-social graph. Secondly, a heterogeneous graph encoder is designed to learn the latent semantics of the views, and cross-view contrastive learning is performed on user and item embeddings to complete self-supervised tasks. Then, weighted fusion is conducted on the user and item embeddings separately for recommendation task. Finally, a multi-task training strategy is employed to jointly optimize self-supervised learning, recommendation and graph masked tasks. Experiments on three real datasets demonstrate a certain performance improvement of SGMN.
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2023, Vol. 36 
