Journal of Guangxi Normal University(Natural Science Edition) ›› 2025, Vol. 43 ›› Issue (2): 133-148.doi: 10.16088/j.issn.1001-6600.2024071801

• Intelligence Information Processing • Previous Articles     Next Articles

Multi-level Attention Networks and Hierarchical Contrastive Learning for Social Recommendation

ZHANG Lijie, WANG Shaoqing*, ZHANG Yao, SUN Fuzhen   

  1. School of Computer Science and Technology, Shandong University of Technology, Zibo Shandong 255000, China
  • Received:2024-07-18 Revised:2024-09-19 Online:2025-03-05 Published:2025-04-02

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Abstract: Incorporating social relationships into recommender systems can effectively improve recommendation quality. However, real-life interactions among users are sparse and complex. The effective utilization of social information is a key issue. The impact of high-order friends is not fully explored by existing social recommendation models, and the strength of relationships between users and the impact of different kinds of relationships on users are ignored, leading to sub-optimal recommendation performance. To address these issues, Multi-level attention networks and Hierarchical Contrastive Learning for social recommendation (MHCL) is proposed. Specifically, the user-level hypergraph is first constructed based on different relationships between users to expand the perceptual scope of node aggregation and deepen the depth of the model. Then, a multilevel attention network is designed to better capture the relationships and importance between user interaction data, where the influence of friends on the user and the degree of inter-item relatedness are captured by the view-level self-attention mechanism, and the influence of different kinds of relationships on the user is adaptively adjusted by the channel-level attention. Meanwhile, hierarchical contrast learning is introduced to augment the data, including the first level of contrast learning between and across views and the second level of contrast learning for high-order relationships, to capture the subtle gaps and high-level abstract features of the data in multiple dimensions. Finally, the proposed model is evaluated on four publicly available benchmark datasets, and the effectiveness and reasonableness of MHCL are validated by the evaluation results. Social denoising will be the focus of future research to improve recommendation systems based on hypergraph neural networks.

Key words: social recommendation, attention networks, hypergraph learning, contrastive learning, recommender systems

CLC Number:  TP391.1
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