Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to improve user-item interaction modeling. Recently, Self-Supervised Learning (SSL) has proven to be remarkably effective in addressing data sparsity through augmented learning tasks. Inspired by this, researchers have attempted to incorporate SSL into social recommendation by supplementing the primary supervised task with social-aware self-supervised signals. However, social information can be unavoidably noisy in characterizing user preferences due to the ubiquitous presence of interest-irrelevant social connections, such as colleagues or classmates who do not share many common interests. To address this challenge, we propose a novel social recommender called the Denoised Self-Augmented Learning paradigm (DSL). Our model not only preserves helpful social relations to enhance user-item interaction modeling but also enables personalized cross-view knowledge transfer through adaptive semantic alignment in embedding space. Our experimental results on various recommendation benchmarks confirm the superiority of our DSL over state-of-the-art methods. We release our model implementation at: https://github.com/HKUDS/DSL.

翻译：社交推荐在包括电子商务和在线流媒体在内的各类在线应用中日益受到关注，其通过利用社交信息来改进用户-物品交互建模。近年来，自监督学习（SSL）已被证明能通过增强学习任务有效解决数据稀疏性问题。受此启发，研究者尝试将SSL融入社交推荐，通过引入社交感知的自监督信号来补充主监督任务。然而，由于兴趣无关的社交连接（如缺乏共同偏好的同事或同学）普遍存在，社交信息在刻画用户偏好时不可避免地存在噪声。为解决这一挑战，我们提出一种新型社交推荐模型——降噪自增强学习范式（DSL）。该模型不仅保留有益社交关系以增强用户-物品交互建模，还能通过嵌入空间的自适应语义对齐实现个性化跨视图知识迁移。在多个推荐基准上的实验结果表明，我们的DSL显著优于现有最先进方法。模型实现已开源：https://github.com/HKUDS/DSL。