Attention-based Sequential Recommendation
Attention-Based Sequential Recommendation is a sequential recommendation method that employs attention mechanisms to selectively model the influence of items in a user's interaction sequence by assigning varying importance weights to different historical interactions.
78
Publications
10.2K
Citations
346
Authors
122
Institutions
Publications per year
2017–2026
78
Parents
78
Self-Attentive Sequential Recommendation
Wang-Cheng Kang, Julian McAuley · 2018 · 2.7K citations
Time Interval Aware Self-Attention for Sequential Recommendation
Jiacheng Li, Yujie Wang, Julian McAuley · 2020 · 613 citations · Full text
Sequential Recommender System based on Hierarchical Attention Networks
Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang et al. · 2018 · 369 citations · Full text
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122

The University of Queensland
Brisbane, Australia
6 h-index
14 publications
2.1K citations
Tsinghua University
Beijing, China
5 h-index
16 publications
798 citations
University of California San Diego
San Diego, United States
5 h-index
10 publications
7.1K citations

Xi'an Jiaotong University
Xi'an, China
4 h-index
14 publications
627 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
4 publications
143 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
4 h-index
7 publications
461 citations
University of Technology Sydney
Sydney, Australia
4 h-index
6 publications
413 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
4 h-index
7 publications
341 citations
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4 publications · 114 citations
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Conference · 4 publications · 332 citations
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3 publications · 185 citations
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Conference · 3 publications · 233 citations
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2 publications · 61 citations
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