Data Sparsity Handling
Data Sparsity Handling is a methodological approach in session-based recommendation systems that addresses the challenge of limited user interaction data by exploiting structural, sequential, or semantic patterns to improve recommendation accuracy in sparse data environments.
345
Publications
25.8K
Citations
1.2K
Authors
390
Institutions
Publications per year
2017–2026
202
Parents
345
Hao Ma, Haixuan Yang, Michael R. Lyu et al. · 2008 · 1.4K citations
A biterm topic model for short texts
Xiaohui Yan, Jiafeng Guo, Yanyan Lan et al. · 2013 · 1.1K citations
SLIM: Sparse Linear Methods for Top-N Recommender Systems
Xia Ning, George Karypis · 2011 · 738 citations
1–5 of 345
1.2K
Hong Kong University of Science and Technology, Hong Kong SAR China
8 publications · 763 citations
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6
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5
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5
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4
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1–5 of 1.2K
390
Tsinghua University
Beijing, China
13 h-index
40 publications
2.2K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
10 h-index
23 publications
1.8K citations

Shanghai Jiao Tong University
Shanghai, China
9 h-index
24 publications
1.5K citations
Tencent (China)
Shenzhen, China
8 h-index
19 publications
1.5K citations

University of Electronic Science and Technology of China
Chengdu, China
8 h-index
20 publications
1K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
8 h-index
25 publications
8.1K citations
University of Technology Sydney
Sydney, Australia
7 h-index
15 publications
567 citations

Zhejiang University
Hangzhou, China
6 h-index
16 publications
1.3K citations
1–8 of 390
Repository · 19 publications · 711 citations
12
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Conference · 9 publications · 507 citations
9
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9 publications · 249 citations
9
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TKDE · 7 publications · 484 citations
7
h-index
5 publications · 563 citations
5
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1–5