Recommendation Diversification
Recommendation Diversification is a recommendation strategy that enhances the variety of suggested items by deliberately reducing redundancy and promoting coverage across different categories, attributes, or user interests within a ranked list.
69
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4.4K
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209
Authors
109
Institutions
Publications per year
2017–2026
31
Variants
Recommender System Diversification
69
Improving recommendation lists through topic diversification
Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan et al. · 2005 · 1.9K citations
It takes variety to make a world
Cong Yu, Laks V. S. Lakshmanan, Sihem Amer-Yahia · 2009 · 167 citations · Full text
DGCN: Diversified Recommendation with Graph Convolutional Networks
Yu Zheng, Chen Gao, Liang Chen et al. · 2021 · 130 citations · Full text
Adaptive diversification of recommendation results via latent factor portfolio
Yue Shi, Xiaoxue Zhao, Jun Wang et al. · 2012 · 87 citations
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University College Dublin
Dublin, Ireland
5 h-index
9 publications
286 citations
Yahoo (United States)
Sunnyvale, United States
3 h-index
5 publications
538 citations
Eindhoven University of Technology
Eindhoven, The Netherlands
3 h-index
4 publications
188 citations

Sun Yat-sen University
Guangzhou, China
3 h-index
6 publications
227 citations

University of Minnesota
Minneapolis, United States
3 h-index
4 publications
3.9K citations
Fudan University
Shanghai, China
3 h-index
5 publications
200 citations

University of Electronic Science and Technology of China
Chengdu, China
2 h-index
4 publications
134 citations

Tel Aviv University
Tel Aviv, Israel
2 h-index
6 publications
225 citations
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3 publications · 164 citations
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2 publications · 39 citations
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2 publications · 21 citations
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Repository · 2 publications · 40 citations
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Repository · 2 publications · 38 citations
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