Cross-platform Recommendation Systems
Cross-Platform Recommendation Systems is a recommendation framework that integrates user interaction data across multiple distinct platforms to generate personalized recommendations by modeling cross-platform user behaviors and preferences, thereby extending traditional recommendation systems through the explicit alignment and transfer of user-item interaction patterns across heterogeneous digital environments.
16
Publications
1K
Citations
59
Authors
29
Institutions
Publications per year
2017–2026
10
Parents
16
Cross-domain Recommendation Without Sharing User-relevant Data
Chen Gao, Xiangning Chen, Fuli Feng et al. · 2019 · 127 citations · Full text
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29

Chinese Academy of Sciences
Beijing, China
3 h-index
9 publications
540 citations
Tsinghua University
Beijing, China
3 h-index
13 publications
976 citations
Institute of Automation
Beijing, China
2 h-index
7 publications
434 citations
Shandong Institute of Automation
Jinan, China
2 h-index
6 publications
330 citations
National University of Singapore
Singapore, Singapore
2 h-index
4 publications
464 citations

Indian Institute of Management Indore
Indore, India
1 h-index
1 publications
97 citations

King Saud University
Riyadh, Saudi Arabia
1 h-index
1 publications
70 citations

Cornell University
Ithaca, United States
1 h-index
1 publications
15 citations
1–8 of 29
TKDE · 1 publications · 34 citations
1
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1 publications · 15 citations
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TOMCCAP · 1 publications · 56 citations
1
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1 publications · 97 citations
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NSE · 1 publications · 139 citations
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1–5