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Conference Paper: Denoised Self-Augmented Learning for Social Recommendation
Title | Denoised Self-Augmented Learning for Social Recommendation |
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Authors | |
Issue Date | 20-Apr-2023 |
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. |
Persistent Identifier | http://hdl.handle.net/10722/333823 |
DC Field | Value | Language |
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dc.contributor.author | Wang, Tianle | - |
dc.contributor.author | Xia, Lianghao | - |
dc.contributor.author | Huang, Chao | - |
dc.date.accessioned | 2023-10-06T08:39:22Z | - |
dc.date.available | 2023-10-06T08:39:22Z | - |
dc.date.issued | 2023-04-20 | - |
dc.identifier.uri | http://hdl.handle.net/10722/333823 | - |
dc.description.abstract | <p><span>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.</span><br></p> | - |
dc.language | eng | - |
dc.relation.ispartof | International Joint Conference on Artificial Intelligence - IJCAI 2023 (19/08/2023-25/08/2023, Macau) | - |
dc.title | Denoised Self-Augmented Learning for Social Recommendation | - |
dc.type | Conference_Paper | - |
dc.identifier.doi | 10.24963/ijcai.2023/258 | - |
dc.identifier.spage | 2324 | - |
dc.identifier.epage | 2331 | - |