Autoencoder-based Recommendation
Autoencoder-Based Recommendation is a recommendation method that employs autoencoder architectures to model user-item interactions by reconstructing observed preference data, thereby capturing latent representations distinct from other collaborative filtering approaches through nonlinear dimensionality reduction.
150
Publications
9.6K
Citations
514
Authors
218
Institutions
Publications per year
2017–2026
140
Parents
150
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
Yao Wu, Christopher DuBois, Alice X. Zheng et al. · 2016 · 1K citations
Embedding-based News Recommendation for Millions of Users
Shumpei Okura, Yukihiro Tagami, Shingo Ono et al. · 2017 · 509 citations
Collaborative Variational Autoencoder for Recommender Systems
Xiaopeng Li, James She · 2017 · 455 citations
Embarrassingly Shallow Autoencoders for Sparse Data
Harald Steck · 2019 · 266 citations · Full text
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218

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
5 h-index
11 publications
1.6K citations
Tsinghua University
Beijing, China
5 h-index
10 publications
666 citations

University of Electronic Science and Technology of China
Chengdu, China
5 h-index
14 publications
888 citations
Tencent (China)
Shenzhen, China
5 h-index
15 publications
833 citations
Xiamen University
Xiamen, China
4 h-index
10 publications
308 citations
Wuhan University
Wuhan, China
4 h-index
10 publications
845 citations

Chinese Academy of Sciences
Beijing, China
4 h-index
8 publications
535 citations

McGill University
Montreal, Canada
3 h-index
12 publications
772 citations
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16 publications · 451 citations
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Repository · 10 publications · 958 citations
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7 publications · 238 citations
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Conference · 5 publications · 557 citations
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5 publications · 167 citations
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