Shared Latent Space Learning
Shared Latent Space Learning is a cross-domain recommendation method that projects user and item representations from multiple domains into a common latent space, enabling knowledge transfer by aligning structural and semantic patterns across domains while preserving domain-specific characteristics.
147
Publications
11.4K
Citations
502
Authors
216
Institutions
Publications per year
2017–2026
113
147
Adversarial Multi-task Learning for Text Classification
Pengfei Liu, Xipeng Qiu, Xuanjing Huang · 2017 · 596 citations · Full text
Unsupervised Image-to-Image Translation Networks
Ming-Yu Liu · arXiv (Cornell University) · 2017 · 519 citations · Full text
Style Transfer from Non-Parallel Text by Cross-Alignment
Tianxiao Shen, Tao Leí, Regina Barzilay et al. · arXiv (Cornell University) · 2017 · 442 citations · Full text
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Tsinghua University
Beijing, China
9 h-index
24 publications
1.4K citations

Chinese Academy of Sciences
Beijing, China
9 h-index
30 publications
1.3K citations
University of Chinese Academy of Sciences
Beijing, China
8 h-index
23 publications
1.1K citations
Rutgers, The State University of New Jersey
New Brunswick, United States
6 h-index
10 publications
273 citations
New York University
New York, United States
5 h-index
10 publications
798 citations
Institute of Computing Technology
Beijing, China
5 h-index
16 publications
474 citations
University of Technology Sydney
Sydney, Australia
5 h-index
11 publications
945 citations

Zhejiang University
Hangzhou, China
4 h-index
11 publications
401 citations
1–8 of 216
Repository · 17 publications · 1.8K citations
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TKDE · 7 publications · 472 citations
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6 publications · 166 citations
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5 publications · 401 citations
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Conference · 5 publications · 461 citations
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