In the information age, recommendation systems are vital for efficiently filtering information and identifying user preferences. Online social platforms have enriched these systems by providing valuable auxiliary information. Socially connected users are assumed to share similar preferences, enhancing recommendation accuracy and addressing cold start issues. However, empirical findings challenge the assumption, revealing that certain social connections can actually harm system performance. Our statistical analysis indicates a significant amount of noise in the social network, where many socially connected users do not share common interests. To address this issue, we propose an innovative \underline{I}nterest-aware \underline{D}enoising and \underline{V}iew-guided \underline{T}uning (IDVT) method for the social recommendation. The first ID part effectively denoises social connections. Specifically, the denoising process considers both social network structure and user interaction interests in a global view. Moreover, in this global view, we also integrate denoised social information (social domain) into the propagation of the user-item interactions (collaborative domain) and aggregate user representations from two domains using a gating mechanism. To tackle potential user interest loss and enhance model robustness within the global view, our second VT part introduces two additional views (local view and dropout-enhanced view) for fine-tuning user representations in the global view through contrastive learning. Extensive evaluations on real-world datasets with varying noise ratios demonstrate the superiority of IDVT over state-of-the-art social recommendation methods.

翻译：在信息时代，推荐系统对于高效过滤信息、识别用户偏好至关重要。在线社交平台通过提供有价值的辅助信息，进一步丰富了这些系统。传统假设认为社交关联用户具有相似偏好，这有助于提升推荐精度并缓解冷启动问题。然而实证研究对该假设提出了挑战，表明某些社交关联实际上可能损害系统性能。我们的统计分析指出社交网络中存在着显著噪声，许多社交关联用户并未共享共同兴趣。为解决该问题，我们提出一种创新的兴趣感知去噪与多视角引导调优（IDVT）方法用于社交推荐。其中ID部分首先对社交关联进行有效去噪：该去噪过程在全局视角下同时考量社交网络结构与用户交互兴趣。此外，在此全局视角中，我们还将去噪后的社交信息（社交域）整合到用户-物品交互（协同域）的传播过程中，并通过门控机制聚合来自两个领域的用户表征。针对全局视角中可能存在的用户兴趣损失问题，为增强模型鲁棒性，VT部分引入两个补充视角（局部视角与丢弃增强视角），通过对比学习对全局视角中的用户表征进行精细化调优。在具有不同噪声比例的真实数据集上的大量实验表明，IDVT方法优于当前最先进的社交推荐方法。