Sparse Interaction Recommendation is a recommendation paradigm within the broader class of collaborative filtering methods that specifically addresses the challenge of making accurate predictions when user-item interaction data are extremely limited or sparsely observed.
17
Publications
702
Citations
66
Authors
27
Institutions
Publications per year
2017–2026
14
Parents
Publications
17
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation
2021 · 132 citations · Full text
1–5 of 17
Authors
66
- YX
3
h-index
- DY
3
h-index
- MKMaciej Kula
2 publications · 164 citations
2
h-index
- ZW
1
h-index
- JS
1
h-index
1–5 of 66
Institutions
27
Fudan University
Shanghai, China
3 h-index
13 publications
168 citations
Tsinghua University
Beijing, China
2 h-index
7 publications
157 citations
Shandong University
Jinan, China
2 h-index
6 publications
144 citations
Tencent (China)
Shenzhen, China
1 h-index
5 publications
100 citations
The University of Queensland
Brisbane, Australia
1 h-index
2 publications
264 citations
Qilu University of Technology
Jinan, China
1 h-index
1 publications
26 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
1 h-index
1 publications
25 citations
University of Illinois System
Urbana, United States
1 h-index
1 publications
23 citations
1–8 of 27
Venues
- IEEE Transactions on Automation Science and Engineering
1 publications · 21 citations
1
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- arXiv (Cornell University)
Repository · 1 publications · 80 citations
1
h-index
- ACM Transactions on Information Systems
1 publications · 88 citations
1
h-index
- Proceedings of the 31st ACM International Conference on Information & Knowledge Management
Conference · 1 publications · 90 citations
1
h-index
- Conference on Recommender Systems
Conference · 1 publications · 84 citations
1
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1–5