{"type":"ScholarlyArticle","title":"Denoised Self-Augmented Learning for Social Recommendation","authors":[{"name":"Tianle Wang","slug":"tianle-wang","given":"Tianle","family":"Wang","affiliation":"University of Hong Kong , Hong Kong"},{"name":"Lianghao Xia","slug":"lianghao-xia","given":"Lianghao","family":"Xia","affiliation":"University of Hong Kong , Hong Kong"},{"name":"Chao Huang","slug":"chao-huang","given":"Chao","family":"Huang","affiliation":"University of Hong Kong , Hong Kong"}],"abstract":"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.","doi":"10.24963/ijcai.2023/258","url":"https://ijcai.org/proceedings/2023/258","pdf":"https://ijcai.org/proceedings/2023/0258.pdf","markdown":"https://ijcai.org/proceedings/2023/258.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/258","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":258,"pages":{"first":2324,"last":2331},"keywords":["Data Mining: DM: Recommender systems","Data Mining: DM: Information retrieval"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2023] Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 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