Data Sparsity Handling

Data Sparsity Handling is a methodological approach in session-based recommendation systems that addresses the challenge of limited user interaction data by exploiting structural, sequential, or semantic patterns to improve recommendation accuracy in sparse data environments.

345

Publications

25.8K

Citations

1.2K

Authors

390

Institutions

Publications per year

2017–2026

202

Authors

1.2K

  1. QY
    Qiang Yang

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

    8 publications · 763 citations

    8

    h-index

  2. MR
    Michael R. Lyu

    Chinese University of Hong Kong, Hong Kong SAR China

    6 publications · 1.9K citations

    6

    h-index

  3. GK
    George Karypis

    University of Minnesota, United States

    5 publications · 1.6K citations

    5

    h-index

  4. IK
    Irwin King

    Chinese University of Hong Kong, Hong Kong SAR China

    5 publications · 1.9K citations

    5

    h-index

  5. HS
    Haşim Sak

    Google (United States), United States

    4 publications · 378 citations

    4

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 711 citations

    12

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 507 citations

    9

    h-index

  3. IEEE Access

    9 publications · 249 citations

    9

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 484 citations

    7

    h-index

  5. Expert Systems with Applications

    5 publications · 563 citations

    5

    h-index

1–5