Multi-view Recommendation

Multi-View Recommendation is a recommendation framework that integrates multiple heterogeneous data sources or perspectives—such as user-item interactions, social networks, and content features—by modeling them as distinct yet complementary views, thereby improving recommendation accuracy through synergistic learning across views.

65

Publications

3.2K

Citations

255

Authors

104

Institutions

Publications per year

2017–2026

56

Authors

255

  1. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    4 publications · 238 citations

    4

    h-index

  2. XX
    Xin Xia

    Singapore Management University, Singapore

    3 publications · 258 citations

    3

    h-index

  3. XX
    Xing Xie

    Microsoft Research Asia (China), China

    3 publications · 422 citations

    3

    h-index

  4. PK
    Panos K. Chrysanthis

    University of Pittsburgh, United States

    3 publications · 101 citations

    3

    h-index

  5. MA
    Mohamed A. Sharaf

    The University of Queensland, Australia

    3 publications · 101 citations

    3

    h-index

1–5 of 255

Venues

  1. Neurocomputing

    4 publications · 231 citations

    4

    h-index

  2. Expert Systems with Applications

    3 publications · 95 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 60 citations

    3

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 231 citations

    3

    h-index

  5. ACM Transactions on Information Systems

    2 publications · 87 citations

    2

    h-index

1–5