Contrastive Learning Augmented Social Recommendations

• URL: http://arxiv.org/abs/2502.15695v3
• Date: Thu, 09 Oct 2025 18:02:16 GMT
• Title: Contrastive Learning Augmented Social Recommendations
• Authors: Lin Wang, Weisong Wang, Xuanji Xiao, Qing Li,
• Abstract summary: We propose leveraging the social-relation graph to enrich interest representations from behavior-based models.<n>We employ a dual-view denoising strategy, employing low-rank SVD to the user-item interaction matrix for a denoised social graph.<n>We adopt a "mutual distillation" technique to isolate the original interests into aligned social/behavior interests and social/behavior specific interests.
• Score: 6.597090954336996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems are essential for modern content platforms, yet traditional behavior-based models often struggle with cold users who have limited interaction data. Engaging these users is crucial for platform growth. To bridge this gap, we propose leveraging the social-relation graph to enrich interest representations from behavior-based models. However, extracting value from social graphs is challenging due to relation noise and cross-domain inconsistency. To address the noise propagation and obtain accurate social interest, we employ a dual-view denoising strategy, employing low-rank SVD to the user-item interaction matrix for a denoised social graph and contrastive learning to align the original and reconstructed social graphs. Addressing the interest inconsistency between social and behavioral interests, we adopt a "mutual distillation" technique to isolate the original interests into aligned social/behavior interests and social/behavior specific interests, maximizing the utility of both. Experimental results on widely adopted industry datasets verify the method's effectiveness, particularly for cold users, offering a fresh perspective for future research. The implementation can be accessed at https://github.com/WANGLin0126/CLSRec.

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