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

Authors

712

  1. FW
    Fangzhao Wu

    Tsinghua University, China

    9 publications · 873 citations

    9

    h-index

  2. CW
    Chuhan Wu

    Tsinghua University, China

    8 publications · 822 citations

    8

    h-index

  3. YH
    Yongfeng Huang

    Tsinghua University, China

    8 publications · 822 citations

    8

    h-index

  4. CX
    Changsheng Xu

    Chinese Academy of Sciences, China

    7 publications · 706 citations

    7

    h-index

  5. LN
    Liqiang Nie

    Shandong University, China

    6 publications · 501 citations

    6

    h-index

1–5 of 712

Venues

  1. IEEE Transactions on Multimedia

    12 publications · 716 citations

    12

    h-index

  2. arXiv (Cornell University)

    Repository · 10 publications · 326 citations

    9

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 431 citations

    6

    h-index

  4. IEEE Access

    4 publications · 90 citations

    4

    h-index

  5. Applied Intelligence

    3 publications · 78 citations

    3

    h-index

1–5