Self-supervised Sequential Recommendation
Self-Supervised Sequential Recommendation is a sequential recommendation framework that leverages self-supervised learning to model user-item interaction sequences by generating supervisory signals from the raw data itself, thereby improving sequential pattern learning without relying on explicit auxiliary information or external annotations.
24
Publications
2.3K
Citations
105
Authors
31
Institutions
Publications per year
2017–2026
24
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Intent Contrastive Learning for Sequential Recommendation
Yongjun Chen, Zhiwei Liu, Jia Li et al. · Proceedings of the ACM Web Conference 2022 · 2022 · 374 citations · Full text
Disentangled Self-Supervision in Sequential Recommenders
Jianxin Ma, Chang Zhou, Hongxia Yang et al. · 2020 · 227 citations
Self-Supervised Reinforcement Learning for Recommender Systems
Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis et al. · 2020 · 213 citations · Full text
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li et al. · arXiv (Cornell University) · 2021 · 83 citations · Full text
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Salesforce (United States)
San Francisco, United States
4 h-index
9 publications
1.8K citations

University of Hong Kong
Pok Fu Lam, Hong Kong
4 h-index
13 publications
589 citations
Google (United Kingdom)
London, United Kingdom
3 h-index
3 publications
279 citations
Telefonica Research and Development
Madrid, Spain
3 h-index
3 publications
279 citations
Tsinghua University
Beijing, China
3 h-index
17 publications
1.5K citations

University of Glasgow
Glasgow, United Kingdom
3 h-index
5 publications
509 citations
Shandong University
Jinan, China
3 h-index
8 publications
317 citations
University of Illinois Chicago
Chicago, United States
3 h-index
4 publications
191 citations
1–8 of 31
Repository · 4 publications · 126 citations
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Conference · 2 publications · 49 citations
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2 publications · 90 citations
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Conference · 2 publications · 76 citations
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Conference · 1 publications · 49 citations
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