Transformer-based Sequential Recommendation
Transformer-Based Sequential Recommendation is a sequential recommendation framework that employs the transformer architecture to model user behavior sequences by leveraging self-attention mechanisms for capturing long-range dependencies and complex sequential patterns in user-item interactions.
48
Publications
3.7K
Citations
245
Authors
86
Institutions
Publications per year
2017–2026
48
Parents
48
Behavior sequence transformer for e-commerce recommendation in Alibaba
Qiwei Chen, Huan Zhao, Wei Li et al. · 2019 · 388 citations
SSE-PT: Sequential Recommendation Via Personalized Transformer
Liwei Wu, Shuqing Li, Cho‐Jui Hsieh et al. · 2020 · 248 citations · Full text
Sequential Recommendation via Stochastic Self-Attention
Ziwei Fan, Zhiwei Liu, Yu Wang et al. · Proceedings of the ACM Web Conference 2022 · 2022 · 184 citations
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University of Illinois Chicago
Chicago, United States
5 h-index
16 publications
1.6K citations

City University of Hong Kong
Hong Kong, Hong Kong
4 h-index
18 publications
790 citations
Tsinghua University
Beijing, China
4 h-index
14 publications
667 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
3 h-index
6 publications
268 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
3 h-index
5 publications
249 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
3 h-index
6 publications
697 citations

Beihang University
Beijing, China
3 h-index
3 publications
251 citations

University of Amsterdam
Amsterdam, The Netherlands
3 h-index
6 publications
137 citations
1–8 of 86
Repository · 4 publications · 409 citations
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Conference · 3 publications · 173 citations
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Conference · 2 publications · 435 citations
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Conference · 2 publications · 136 citations
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3 publications · 69 citations
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