Cross-domain Sequential Recommendation
Cross-Domain Sequential Recommendation is a sequential recommendation framework that leverages sequential user behavior from one domain to improve recommendation accuracy in another domain by transferring knowledge across domains with distinct item spaces.
21
Publications
1.4K
Citations
91
Authors
32
Institutions
Publications per year
2017–2026
21
Parents
21
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
Yupeng Hou, Zhankui He, Julian McAuley et al. · 2023 · 130 citations
Muyang Ma, Pengjie Ren, Yujie Lin et al. · 2019 · 113 citations
1–5 of 21
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32

Shandong Normal University
Jinan, China
5 h-index
14 publications
791 citations

The University of Queensland
Brisbane, Australia
4 h-index
8 publications
456 citations

Zhejiang University
Hangzhou, China
4 h-index
12 publications
623 citations
Shandong University
Jinan, China
3 h-index
17 publications
1.2K citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
9 publications
467 citations

University of Amsterdam
Amsterdam, The Netherlands
3 h-index
4 publications
337 citations
Microsoft Research Asia (China)
Beijing, China
2 h-index
3 publications
44 citations

Alibaba Group (China)
Hangzhou, China
2 h-index
6 publications
336 citations
1–8 of 32
TKDE · 2 publications · 112 citations
2
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Repository · 1 publications · 15 citations
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Conference · 1 publications · 60 citations
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1 publications · 14 citations
1
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Conference · 1 publications · 250 citations
1
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1–5