Cold-start Session Recommendation
Cold-Start Session Recommendation is a session-based recommendation approach that specifically addresses the challenge of making accurate recommendations in sessions where no prior interaction history is available, thereby distinguishing itself from general session-based methods by focusing on initial-user or first-time interaction scenarios.
11
Publications
336
Citations
48
Authors
29
Institutions
Publications per year
2017–2026
7
Parents
11
Large Language Model Simulator for Cold-Start Recommendation
Feiran Huang, Yuanchen Bei, Zhenghang Yang et al. · 2025 · 55 citations · Full text
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29
University of Illinois Chicago
Chicago, United States
3 h-index
3 publications
102 citations

Central South University
Changsha, China
1 h-index
1 publications
55 citations
Nanjing University of Science and Technology
Nanjing, China
1 h-index
5 publications
125 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
5 publications
115 citations

Alibaba Group (China)
Hangzhou, China
1 h-index
1 publications
55 citations
Ministry of Education of the People's Republic of China
Beijing, China
1 h-index
5 publications
230 citations
Jilin Province Science and Technology Department
Changchun, China
1 h-index
4 publications
184 citations

Ghent University
Ghent, Belgium
1 h-index
3 publications
48 citations
1–8 of 29
2 publications · 71 citations
2
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1 publications · 15 citations
1
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1 publications · 23 citations
1
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1 publications · 41 citations
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Conference · 1 publications · 38 citations
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