User Embeddings is a latent representation of a user in a continuous vector space that captures individual preferences, behaviors, or characteristics derived from interaction data, distinguished from other user representations by its use of distributed, low-dimensional encoding to model nuanced aspects of user identity for personalized search and user modeling.
145
Publications
7.5K
Citations
598
Authors
198
Institutions
Publications per year
2017–2026
131
Publications
145
Neural Graph Collaborative Filtering
2019 · 3K citations · Full text
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
2019 · 625 citations · Full text
1–5 of 145
Authors
598
- FW
8
h-index
- CW
8
h-index
- YH
8
h-index
- XX
6
h-index
- PS
4
h-index
1–5 of 598
Institutions
198
Tsinghua University
Beijing, China
10 h-index
29 publications
1.3K citations
Microsoft Research Asia (China)
Beijing, China
9 h-index
16 publications
862 citations
Tencent (China)
Shenzhen, China
8 h-index
34 publications
1.7K citations
Peking University
Beijing, China
5 h-index
13 publications
905 citations
Chinese Academy of Sciences
Beijing, China
5 h-index
8 publications
135 citations
Beijing University of Posts and Telecommunications
Beijing, China
5 h-index
12 publications
630 citations
Sun Yat-sen University
Guangzhou, China
5 h-index
9 publications
518 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
9 publications
474 citations
1–8 of 198
Venues
- arXiv (Cornell University)
Repository · 10 publications · 241 citations
10
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- IEEE Transactions on Knowledge and Data Engineering
TKDE · 7 publications · 730 citations
7
h-index
- ACM Transactions on Information Systems
5 publications · 105 citations
5
h-index
- Neural Computing and Applications
3 publications · 63 citations
3
h-index
- Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Conference · 3 publications · 117 citations
3
h-index
1–5