Automatic Self-supervised Learning for Social Recommendations

[2412.18735] Automatic Self-supervised Learning for Social Recommendations Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Information Retrieval arXiv:2412.18735 (cs) [Submitted on 25 Dec 2024 ( v1 ), last revised 10 Apr 2026 (this version, v3)] Title: Automatic Self-supervised Learning for Social Recommendations Authors: Xin He , Wenqi Fan , Mingchen Sun , Ying Wang , Xin Wang View a PDF of the paper titled Automatic Self-supervised Learning for Social Recommendations, by Xin He and 4 other authors View PDF HTML (experimental) Abstract: In recent years, researchers have leveraged social relations to enhance recommendation performance. However, most existing social recommendation methods require carefully designed auxiliary social tasks tailored to specific scenarios, which depend heavily on domain knowledge and expertise. To address this limitation, we propose Automatic Self-supervised Learning for Social Recommendations (AusRec), which integrates multiple self-supervised auxiliary tasks with an automatic weighting mechanism to adaptively balance their contributions through a meta-learning optimization framework. This design enables the model to automatically learn the optimal importance of each auxiliary task, thereby enhancing representation learning in social recommendations. Extensive experiments on several real-world datasets demonstrate that AusRec consistently outperforms state-of-the-art baselines, validating its effectiveness and robustness across different recommendation scenarios. Comments: Accepted by Neurocomputing Subjects: Information Retrieval (cs.IR) ; Machine Learning (cs.LG) Cite as: arXiv:2412.18735 [cs.IR] (or arXiv:2412.18735v3 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2412.18735 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1016/j.neucom.2026.133024 Focus to learn more DOI(s) linking to related resources Submission history From: Xin He [ view email ] [v1] Wed, 25 Dec 2024 01:47:39 UTC (1,766 KB) [v2] Fri, 21 Feb 2025 06:11:41 UTC (1,675 KB) [v3] Fri, 10 Apr 2026 08:58:31 UTC (3,164 KB) Full-text links: Access Paper: View a PDF of the paper titled Automatic Self-supervised Learning for Social Recommendations, by Xin He and 4 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2024-12 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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