Content-based Cold-start Recommendation
Content-Based Cold-Start Recommendation is a recommendation approach that addresses the cold-start problem by leveraging item or user attribute information to generate personalized suggestions without relying on historical interaction data.
31
Publications
1.1K
Citations
111
Authors
55
Institutions
Publications per year
2017–2026
22
Parents
31
A unified approach to building hybrid recommender systems
Asela Gunawardana, Christopher Meek · 2009 · 154 citations
Aligning Distillation For Cold-start Item Recommendation
Feiran Huang, Zefan Wang, Xiao Huang et al. · 2023 · 92 citations
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Laboratoire Lorrain de Recherche en Informatique et ses Applications
Nancy, France
2 h-index
4 publications
79 citations

Akdeniz University
Antalya, Türkiye
2 h-index
2 publications
21 citations
Eskisehir Technical University
Eskişehir, Türkiye
2 h-index
4 publications
42 citations
Université de Lorraine
Nancy, France
2 h-index
4 publications
79 citations

Universitat Pompeu Fabra
Barcelona, Spain
2 h-index
2 publications
99 citations
Politecnico di Milano
Milan, Italy
2 h-index
13 publications
511 citations
Tsinghua University
Beijing, China
2 h-index
9 publications
119 citations
Islamic Azad University of Chalous
Chālūs, Iran
1 h-index
1 publications
43 citations
1–8 of 55
MPE · 1 publications · 43 citations
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1 publications · 15 citations
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1 publications · 18 citations
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TIIS · 1 publications · 26 citations
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1 publications · 25 citations
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