Cross-platform Recommendation Systems

Cross-Platform Recommendation Systems is a recommendation framework that integrates user interaction data across multiple distinct platforms to generate personalized recommendations by modeling cross-platform user behaviors and preferences, thereby extending traditional recommendation systems through the explicit alignment and transfer of user-item interaction patterns across heterogeneous digital environments.

16

Publications

1K

Citations

59

Authors

29

Institutions

Publications per year

2017–2026

10

Authors

59

  1. CX
    Changsheng Xu

    Chinese Academy of Sciences, China

    4 publications · 236 citations

    4

    h-index

  2. YL
    Yong Li

    Tsinghua University, China

    3 publications · 218 citations

    3

    h-index

  3. CG
    Chen Gao

    Tsinghua University, China

    3 publications · 218 citations

    3

    h-index

  4. JS
    Jitao Sang

    Chinese Academy of Sciences, China

    3 publications · 166 citations

    3

    h-index

  5. ZD
    Zhengyu Deng

    Institute of Automation, China

    2 publications · 100 citations

    2

    h-index

1–5 of 59

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 34 citations

    1

    h-index

  2. Technological Forecasting and Social Change

    1 publications · 15 citations

    1

    h-index

  3. 1

    h-index

  4. Information & Management

    1 publications · 97 citations

    1

    h-index

  5. IEEE Transactions on Network Science and Engineering

    NSE · 1 publications · 139 citations

    1

    h-index

1–5