Content Representation Learning
Content Representation Learning is a machine learning approach that, within the domain of content-based recommendation, specifically focuses on automatically extracting and structuring informative, latent feature representations from raw content data to enable effective item modeling and personalized user preference prediction.
205
Publications
11.3K
Citations
712
Authors
239
Institutions
Publications per year
2017–2026
127
Parents
205
Collaborative Variational Autoencoder for Recommender Systems
Xiaopeng Li, James She · 2017 · 455 citations
Neural News Recommendation with Attentive Multi-View Learning
Chuhan Wu, Fangzhao Wu, Mingxiao An et al. · 2019 · 253 citations · Full text
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Tsinghua University
Beijing, China
13 h-index
29 publications
2.7K citations
University of Science and Technology of China
Hefei, China
11 h-index
18 publications
1.3K citations
Microsoft Research Asia (China)
Beijing, China
10 h-index
18 publications
1.3K citations
National University of Singapore
Singapore, Singapore
7 h-index
23 publications
2.8K citations

Peking University
Beijing, China
7 h-index
15 publications
667 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
15 publications
466 citations
Wuhan University
Wuhan, China
6 h-index
12 publications
754 citations
Institute of Automation
Beijing, China
6 h-index
25 publications
1.4K citations
1–8 of 239
12 publications · 716 citations
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Repository · 10 publications · 326 citations
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Conference · 6 publications · 431 citations
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4 publications · 90 citations
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3 publications · 78 citations
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