Long-tail Recommendation
Long-Tail Recommendation is a recommendation approach that targets items from the less popular, niche segment of the item distribution, thereby distinguishing itself from mainstream or popularity-biased recommendation methods by focusing on low-frequency but collectively significant items.
74
Publications
5.1K
Citations
238
Authors
109
Institutions
Publications per year
2017–2026
53
74
The long tail of recommender systems and how to leverage it
Yoon‐Joo Park, Alexander Tuzhilin · 2008 · 437 citations
Challenging the long tail recommendation
Hongzhi Yin, Bin Cui, Jing Li et al. · Proceedings of the VLDB Endowment · 2012 · 275 citations
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109

DePaul University
Chicago, United States
6 h-index
9 publications
1.6K citations
University of Colorado Boulder
Boulder, United States
4 h-index
6 publications
522 citations

University of Electronic Science and Technology of China
Chengdu, China
4 h-index
10 publications
749 citations
Google (United States)
Mountain View, United States
3 h-index
22 publications
2.9K citations
New York University
New York, United States
3 h-index
4 publications
1.3K citations
University of Science and Technology of China
Hefei, China
3 h-index
6 publications
1.1K citations
Tsinghua University
Beijing, China
3 h-index
7 publications
350 citations

University of Colorado System
Boulder, United States
3 h-index
6 publications
524 citations
1–8 of 109
Repository · 9 publications · 401 citations
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TKDE · 4 publications · 442 citations
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4 publications · 261 citations
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3 publications · 119 citations
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Conference · 2 publications · 65 citations
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