Transformer-based Recommendation
Transformer-Based Recommendation is a recommendation approach that employs the transformer architecture—characterized by self-attention mechanisms—to model sequential user-item interactions for generating personalized recommendations.
84
Publications
4.6K
Citations
341
Authors
117
Institutions
Publications per year
2017–2026
84
Parents
84
Behavior sequence transformer for e-commerce recommendation in Alibaba
Qiwei Chen, Huan Zhao, Wei Li et al. · 2019 · 388 citations
Filter-enhanced MLP is All You Need for Sequential Recommendation
Kun Zhou, Hui Yu, Wayne Xin Zhao et al. · Proceedings of the ACM Web Conference 2022 · 2022 · 342 citations · Full text
Personalized re-ranking for recommendation
Changhua Pei, Yi Zhang, Yongfeng Zhang et al. · 2019 · 190 citations
1–5 of 84
341
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Rutgers, The State University of New Jersey, United States
3 publications · 319 citations
3
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3
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3
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1–5 of 341
117
Tsinghua University
Beijing, China
6 h-index
17 publications
868 citations
Wuhan University
Wuhan, China
5 h-index
4 publications
274 citations

City University of Hong Kong
Hong Kong, Hong Kong
5 h-index
18 publications
790 citations
Tencent (China)
Shenzhen, China
4 h-index
2 publications
55 citations

University of Amsterdam
Amsterdam, The Netherlands
4 h-index
6 publications
137 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
4 h-index
6 publications
296 citations
Institute of Computing Technology
Beijing, China
3 h-index
6 publications
183 citations
University of Illinois Chicago
Chicago, United States
3 h-index
11 publications
1.3K citations
1–8 of 117
Repository · 5 publications · 364 citations
6
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Conference · 4 publications · 389 citations
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3 publications · 75 citations
5
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Conference · 3 publications · 137 citations
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Conference · 2 publications · 526 citations
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