A social recommendation model based on cross-view contrastive learning and multi-head attention for multi-rating fusion

Chen, R. and Dai, Z. and Lu, W. and Guo, Y. and Meng, W. and Li, P. and Huang, M. and Kong, X. (2026) A social recommendation model based on cross-view contrastive learning and multi-head attention for multi-rating fusion. Engineering Applications of Artificial Intelligence, 174: 114538. ISSN 0952-1976

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Abstract

In recent years, social recommender systems have become a hot research field. Contrastive learning effectively enhances the expressiveness of user representations by modeling the consistency of representations between interactive views and social views, thereby improving recommendation performance. This paper proposes a social recommendation model based on cross-view contrastive learning, which employs a multi-head attention mechanism to fuse multi-rating information. It adaptively assigns weights to multiple views, making more effective use of rich social relationships and social trust information to alleviate data sparsity. In the rating view, interaction-aware noise with orientation-preserving constraints is introduced for data augmentation. The proposed model constructs a cross-view contrastive learning task between the rating view and the social view. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed approach and its key components, and reveal that our model consistently outperforms state-of-the-art methods.