Cold-start Sequential Recommendation
Cold-Start Sequential Recommendation is a subclass of sequential recommendation that specifically addresses the challenge of generating accurate personalized item sequences for users or items with little to no historical interaction data by incorporating auxiliary information or transferable knowledge to mitigate data sparsity.
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Publications
1.2K
Citations
78
Authors
36
Institutions
Publications per year
2017–2026
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Parents
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Sequential Recommendation via Stochastic Self-Attention
Ziwei Fan, Zhiwei Liu, Yu Wang et al. · Proceedings of the ACM Web Conference 2022 · 2022 · 184 citations
Sequential Scenario-Specific Meta Learner for Online Recommendation
Zhengxiao Du, Xiaowei Wang, Hongxia Yang et al. · 2019 · 105 citations
Learning multiple-question decision trees for cold-start recommendation
Mingxuan Sun, Fuxin Li, Joonseok Lee et al. · 2013 · 78 citations
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36
University of Chinese Academy of Sciences
Beijing, China
4 h-index
10 publications
1K citations
University of Illinois Chicago
Chicago, United States
3 h-index
9 publications
1.3K citations
Tsinghua University
Beijing, China
3 h-index
5 publications
249 citations
Shandong Institute of Automation
Jinan, China
2 h-index
2 publications
90 citations

Alibaba Group (China)
Hangzhou, China
2 h-index
5 publications
339 citations

Renmin University of China
Beijing, China
2 h-index
9 publications
252 citations

Beihang University
Beijing, China
2 h-index
2 publications
229 citations

Sichuan University
Chengdu, China
1 h-index
3 publications
45 citations
1–8 of 36
Repository · 2 publications · 26 citations
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h-index
Conference · 2 publications · 52 citations
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TKDE · 2 publications · 229 citations
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1 publications · 23 citations
1
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1 publications · 68 citations
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1–5