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.

翻译：推荐系统是现代内容平台的核心组件，但传统基于行为数据的模型在处理交互数据有限的冷启动用户时往往表现不佳。激活这类用户对平台增长至关重要。为弥补这一差距，我们提出利用社交关系图来丰富基于行为模型的兴趣表示。然而，由于关系噪声和跨域不一致性，从社交图中提取有效信息具有挑战性。为解决噪声传播问题并获取准确的社交兴趣，我们采用双视图去噪策略：对用户-物品交互矩阵进行低秩SVD分解以获得去噪后的社交图，并利用对比学习对齐原始与重构的社交图。针对社交兴趣与行为兴趣的不一致问题，我们采用"互蒸馏"技术将原始兴趣分解为对齐的社交/行为兴趣和社交/行为专属兴趣，从而最大化两者的效用。在广泛采用的工业数据集上的实验结果验证了该方法的有效性，尤其对冷启动用户提升显著，为未来研究提供了新视角。实现代码可通过 https://github.com/WANGLin0126/CLSRec 获取。