Deep Learning For Recommendation
Deep Learning for Recommendation is a methodological framework within recommendation systems that leverages deep neural architectures to model complex, non-linear user-item interactions by automatically extracting latent features from high-dimensional, heterogeneous data sources.
1.4K
Publications
118.8K
Citations
4.7K
Authors
1.1K
Institutions
Publications per year
2017–2026
1.3K
1.4K
Neural Collaborative Filtering
Xiangnan He, Lizi Liao, Hanwang Zhang et al. · 2017 · 6.6K citations · Full text
Xiangnan He, Kuan Deng, Xiang Wang et al. · 2020 · 4.1K citations
Deep Neural Networks for YouTube Recommendations
Paul Covington, Jay Adams, Emre Sargin · 2016 · 3.4K citations · Full text
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng, Levent Koç, Jeremiah Harmsen et al. · 2016 · 3.3K citations · Full text
Neural Graph Collaborative Filtering
2019 · 3K citations · Full text
1–5 of 1.4K
4.7K
19
h-index
17
h-index
16
h-index
16
h-index
16
h-index
1–5 of 4.7K
1.1K
Tsinghua University
Beijing, China
34 h-index
173 publications
13.3K citations
University of Science and Technology of China
Hefei, China
25 h-index
80 publications
18.1K citations
National University of Singapore
Singapore, Singapore
25 h-index
59 publications
33.2K citations

Chinese Academy of Sciences
Beijing, China
24 h-index
67 publications
4.5K citations

Alibaba Group (China)
Hangzhou, China
24 h-index
134 publications
14.5K citations
Nanyang Technological University
Singapore, Singapore
23 h-index
57 publications
4.9K citations
University of Technology Sydney
Sydney, Australia
23 h-index
45 publications
4K citations
Huawei Technologies (China)
Shenzhen, China
23 h-index
81 publications
15.3K citations
1–8 of 1.1K
Repository · 96 publications · 7K citations
34
h-index
55 publications · 2.8K citations
27
h-index
40 publications · 2.4K citations
26
h-index
58 publications · 2.4K citations
25
h-index
32 publications · 1.6K citations
24
h-index
1–5