User Representation Learning

User Representation Learning is a computational methodology within user modeling that, by contrast to general data representation techniques, specifically focuses on automatically discovering and encoding latent user characteristics from behavioral, demographic, or contextual data into structured, low-dimensional embeddings that preserve predictive and interpretable user traits for downstream personalization tasks.

172

Publications

9.8K

Citations

671

Authors

182

Institutions

Publications per year

2017–2026

142

Authors

671

  1. FW
    Fangzhao Wu

    Tsinghua University, China

    13 publications · 1.3K citations

    12

    h-index

  2. XX
    Xing Xie

    Microsoft Research Asia (China), China

    13 publications · 1.8K citations

    12

    h-index

  3. CW
    Chuhan Wu

    Tsinghua University, China

    12 publications · 1.3K citations

    11

    h-index

  4. YH
    Yongfeng Huang

    Tsinghua University, China

    11 publications · 969 citations

    10

    h-index

  5. MA
    Mingxiao An

    Microsoft Research Asia (China), China

    6 publications · 1K citations

    6

    h-index

1–5 of 671

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 232 citations

    8

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 318 citations

    7

    h-index

  3. IEEE Access

    4 publications · 112 citations

    4

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 181 citations

    4

    h-index

  5. 4

    h-index

1–5