Cross-domain Recommendation

Cross-Domain Recommendation is a recommendation approach that transfers knowledge between distinct domains to improve recommendation accuracy in a target domain by leveraging user-item interaction data from a source domain.

396

Publications

22.2K

Citations

1.3K

Authors

367

Institutions

Publications per year

2017–2026

287

Authors

1.3K

  1. CC
    Chaochao Chen

    Zhejiang University, China

    17 publications · 1.2K citations

    16

    h-index

  2. XZ
    Xiaolin Zheng

    Zhejiang University, China

    11 publications · 674 citations

    11

    h-index

  3. JL
    Jie Lü

    University of Technology Sydney, Australia

    9 publications · 500 citations

    9

    h-index

  4. QY
    Qiang Yang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    9 publications · 1.3K citations

    9

    h-index

  5. YW
    Yan Wang

    Macquarie University, Australia

    8 publications · 821 citations

    8

    h-index

1–5 of 1.3K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 17 publications · 753 citations

    15

    h-index

  2. arXiv (Cornell University)

    Repository · 18 publications · 570 citations

    14

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 709 citations

    13

    h-index

  4. IEEE Access

    13 publications · 381 citations

    12

    h-index

  5. ACM Transactions on Information Systems

    12 publications · 582 citations

    12

    h-index

1–5