Recommender systems play a pivotal role in modern content platforms, yet traditional behavior-based models often face challenges in addressing cold users with sparse interaction data. Engaging these users, however, remains critical for sustaining platform growth. To tackle this issue, we propose leveraging reconstructed social graph to complement interest representations derived from behavioral data. Despite the widespread availability of social graphs on content platforms, their utility is hindered by social-relation noise and inconsistencies between social and behavioral interests. To mitigate noise propagation in graph data and extract reliable social interests, we introduce a dual-view denoising framework. This approach first applies low-rank singular value decomposition (SVD) to the user-item interaction matrix, generating denoised user embeddings for reconstructing the social graph. It then employs contrastive learning to align the original and reconstructed social graphs. To address the discrepancy between social and behavioral interests, we utilize a mutual distillation mechanism that decomposes interests into four subcategories: aligned social/behavioral interests and social/behavioral-specific interests, enabling effective integration of the two. Empirical results demonstrate the efficacy of our method, particularly in improving recommendations for cold users, by combining social and behavioral data. The implementation of our approach is publicly available at https://github.com/WANGLin0126/CLSRec.

翻译：推荐系统在现代内容平台中扮演着关键角色，然而传统基于行为的模型在处理交互数据稀疏的冷启动用户时常常面临挑战。吸引并服务好这些用户对于维持平台增长至关重要。为解决此问题，我们提出利用重构的社交图来补充从行为数据中提取的兴趣表征。尽管社交图在内容平台上广泛存在，但其效用常受社交关系噪声以及社交兴趣与行为兴趣不一致性的制约。为减轻图数据中的噪声传播并提取可靠的社交兴趣，我们引入了一种双视图去噪框架。该方法首先对用户-物品交互矩阵应用低秩奇异值分解，生成去噪后的用户嵌入以重构社交图；随后采用对比学习对齐原始社交图与重构社交图。针对社交兴趣与行为兴趣间的差异，我们利用互蒸馏机制将兴趣分解为四个子类别：对齐的社交/行为兴趣以及社交/行为专属兴趣，从而实现两者的有效融合。实证结果表明，通过结合社交与行为数据，我们的方法能有效提升推荐性能，尤其在对冷启动用户的推荐改进上效果显著。本方法的实现代码已公开于 https://github.com/WANGLin0126/CLSRec。