  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.
