Sparse Data Recommendation

Sparse Data Recommendation is a recommendation approach that addresses the challenge of limited user-item interaction data by leveraging cross-domain knowledge transfer to improve prediction accuracy in domains with insufficient training samples.

366

Publications

28.3K

Citations

1.2K

Authors

359

Institutions

Publications per year

2017–2026

231

Authors

1.2K

  1. JL
    Jie Lü

    University of Technology Sydney, Australia

    8 publications · 265 citations

    8

    h-index

  2. CH
    Chao Huang

    University of Hong Kong, Hong Kong SAR China

    7 publications · 762 citations

    7

    h-index

  3. CC
    Chaochao Chen

    Zhejiang University, China

    7 publications · 545 citations

    7

    h-index

  4. XZ
    Xiaolin Zheng

    Zhejiang University, China

    6 publications · 348 citations

    6

    h-index

  5. HY
    Hongzhi Yin

    The University of Queensland, Australia

    6 publications · 761 citations

    6

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 1.2K citations

    13

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 12 publications · 865 citations

    11

    h-index

  3. IEEE Access

    12 publications · 286 citations

    11

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 10 publications · 1.4K citations

    10

    h-index

  5. ACM Transactions on Information Systems

    7 publications · 404 citations

    7

    h-index

1–5