Session-Based Cold-Start Recommendation is a recommendation approach that addresses the cold-start problem by modeling user preferences exclusively from anonymous, short-term interaction sequences within a single session, without relying on historical user profiles or long-term behavioral data.
9
Publications
239
Citations
34
Authors
31
Institutions
Publications per year
2017–2026
7
Parents
Publications
9
Generative Models for Cold-Start Recommendations
Andrew I. Schein, Alexandrin Popescul, Lyle Ungar et al. · 2001 · 58 citations
1–5 of 9
Authors
34
- LS
1
h-index
- NL
1
h-index
- DM
1
h-index
- WY
1
h-index
- CH
1
h-index
1–5 of 34
Institutions
31
Dalian Neusoft University of Information
Dalian, China
1 h-index
1 publications
46 citations
Jilin University
Changchun, China
1 h-index
4 publications
184 citations
Jilin Province Science and Technology Department
Changchun, China
1 h-index
4 publications
184 citations
Ministry of Education of the People's Republic of China
Beijing, China
1 h-index
5 publications
230 citations
University of Jinan
Jinan, China
1 h-index
1 publications
18 citations
Institute of Automation
Beijing, China
1 h-index
1 publications
16 citations
Ming Chuan University
Taoyuan District, Taiwan
1 h-index
1 publications
13 citations
Centre National de la Recherche Scientifique
Paris, France
1 h-index
2 publications
50 citations
1–8 of 31
Venues
- Neurocomputing
2 publications · 64 citations
2
h-index
- Applied Intelligence
1 publications · 13 citations
1
h-index
- Data Mining and Knowledge Discovery
1 publications · 25 citations
1
h-index
- International Journal of Machine Learning and Cybernetics
1 publications · 18 citations
1
h-index
- IEEE Transactions on Multimedia
1 publications · 16 citations
1
h-index
1–5