Attention-based Recommendation
Attention-Based Recommendation is a recommendation approach that employs attention mechanisms to selectively emphasize influential features or items in user preference modeling, thereby distinguishing itself from other content-based or collaborative filtering methods through its dynamic weighting of input signals.
125
Publications
6.7K
Citations
505
Authors
183
Institutions
Publications per year
2017–2026
113
Parents
125
Neural Attentional Rating Regression with Review-level Explanations
Chong Chen, Min Zhang, Yiqun Liu et al. · 2018 · 627 citations · Full text
Jilin Chen, Rowan Nairn, Les Nelson et al. · 2010 · 415 citations
Neural News Recommendation with Multi-Head Self-Attention
Chuhan Wu, Fangzhao Wu, Suyu Ge et al. · 2019 · 344 citations · Full text
Chuhan Wu, Fangzhao Wu, Mingxiao An et al. · 2019 · 311 citations · Full text
Attentive Group Recommendation
Da Cao, Xiangnan He, Lianhai Miao et al. · 2018 · 273 citations
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183
Tsinghua University
Beijing, China
8 h-index
18 publications
4.1K citations
Microsoft Research Asia (China)
Beijing, China
6 h-index
8 publications
907 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
21 publications
1.4K citations

Zhejiang University
Hangzhou, China
5 h-index
10 publications
357 citations

Shandong Normal University
Jinan, China
5 h-index
23 publications
1.1K citations

Hefei University of Technology
Hefei, China
4 h-index
16 publications
781 citations
Tencent (China)
Shenzhen, China
4 h-index
19 publications
576 citations
National University of Singapore
Singapore, Singapore
3 h-index
3 publications
554 citations
1–8 of 183
8 publications · 231 citations
8
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7 publications · 191 citations
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TKDE · 6 publications · 299 citations
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6 publications · 111 citations
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6 publications · 240 citations
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