Multi-interest Sequential Recommendation
Multi-Interest Sequential Recommendation is a sequential recommendation approach that models users' evolving preferences by capturing multiple distinct interests within their interaction sequences, thereby distinguishing itself from traditional methods that represent users as single, unified interest vectors.
9
Publications
631
Citations
41
Authors
15
Institutions
Publications per year
2017–2026
9
Parents
9
Controllable Multi-Interest Framework for Recommendation
Yukuo Cen, Jianwei Zhang, Xu Zou et al. · 2020 · 308 citations
Routing Micro-videos via A Temporal Graph-guided Recommendation System
Yongqi Li, Meng Liu, Chaoran Cui et al. · 2019 · 63 citations
Multi-interest Diversification for End-to-end Sequential Recommendation
Wanyu Chen, Pengjie Ren, Fei Cai et al. · ACM Transactions on Information Systems · 2021 · 42 citations
1–5 of 9
41
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2
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University at Albany, State University of New York, United States
2 publications · 324 citations
2
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2
h-index
2
h-index
1–5 of 41
15
Tsinghua University
Beijing, China
3 h-index
13 publications
1.2K citations

Alibaba Group (China)
Hangzhou, China
3 h-index
7 publications
1K citations

Fuzhou University
Fuzhou, China
1 h-index
1 publications
52 citations
Wuhan University
Wuhan, China
1 h-index
1 publications
60 citations

Zhejiang University
Hangzhou, China
1 h-index
6 publications
312 citations

China Mobile (China)
Beijing, China
1 h-index
5 publications
170 citations
Shandong University
Jinan, China
1 h-index
5 publications
315 citations
Institute of Information Engineering
Beijing, China
1 h-index
5 publications
200 citations
1–8 of 15
1 publications · 16 citations
1
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Conference · 1 publications · 60 citations
1
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1 publications · 42 citations
1
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Conference · 1 publications · 34 citations
1
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Repository · 1 publications · 16 citations
1
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1–5