Sparse Data Recommendation
Sparse Data Recommendation is a recommendation approach that addresses the challenge of limited user-item interaction data by leveraging cross-domain knowledge transfer to improve prediction accuracy in domains with insufficient training samples.
366
Publications
28.3K
Citations
1.2K
Authors
359
Institutions
Publications per year
2017–2026
231
Parents
366
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng, Levent Koç, Jeremiah Harmsen et al. · 2016 · 3.3K citations · Full text
Collaborative Deep Learning for Recommender Systems
Hao Wang, Naiyan Wang, Dit‐Yan Yeung · 2015 · 1.7K citations
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
Xiangnan He, Hanwang Zhang, Min‐Yen Kan et al. · 2016 · 1K citations
Learning to recommend with social trust ensemble
Hao Ma, Irwin King, Michael R. Lyu · 2009 · 869 citations
SLIM: Sparse Linear Methods for Top-N Recommender Systems
Xia Ning, George Karypis · 2011 · 738 citations
1–5 of 366
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359
Tsinghua University
Beijing, China
18 h-index
76 publications
3.7K citations
University of Technology Sydney
Sydney, Australia
14 h-index
41 publications
1.7K citations

Beijing University of Posts and Telecommunications
Beijing, China
12 h-index
36 publications
2.2K citations

Zhejiang University
Hangzhou, China
11 h-index
39 publications
1.5K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
10 h-index
23 publications
6.9K citations

University of Electronic Science and Technology of China
Chengdu, China
10 h-index
27 publications
716 citations
Tencent (China)
Shenzhen, China
10 h-index
26 publications
1.5K citations
Shandong University
Jinan, China
9 h-index
21 publications
1.4K citations
1–8 of 359
Repository · 19 publications · 1.2K citations
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TKDE · 12 publications · 865 citations
11
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12 publications · 286 citations
11
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Conference · 10 publications · 1.4K citations
10
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7 publications · 404 citations
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