Cold-start Item Modeling
Cold-Start Item Modeling is an academic research area within item modeling that focuses on developing methods to represent and predict item properties or behaviors in recommendation systems when little to no historical interaction data is available for the item.
9
Publications
1.1K
Citations
45
Authors
21
Institutions
Publications per year
2017–2026
5
Parents
9
45
1
h-index
1
h-index
1
h-index
1
h-index
Laboratoire Lorrain de Recherche en Informatique et ses Applications, France
1 publications · 18 citations
1
h-index
1–5 of 45
21

Shanghai Jiao Tong University
Shanghai, China
2 h-index
6 publications
1.5K citations

National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
Kyiv, Ukraine
1 h-index
1 publications
18 citations

East China Normal University
Shanghai, China
1 h-index
1 publications
17 citations

Hanyang University
Seoul, South Korea
1 h-index
3 publications
48 citations
IBM Research (China)
Beijing, China
1 h-index
1 publications
17 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
1 h-index
1 publications
83 citations
Texas A&M University
College Station, United States
1 h-index
3 publications
114 citations
Wuhan University
Wuhan, China
1 h-index
2 publications
166 citations
1–8 of 21
2 publications · 730 citations
2
h-index
1 publications · 57 citations
1
h-index
Conference · 1 publications · 38 citations
1
h-index
1 publications · 18 citations
1
h-index
TKDE · 1 publications · 83 citations
1
h-index
1–5