Multi-context Fusion

Multi-Context Fusion is a context-aware recommendation method that integrates multiple contextual dimensions—such as temporal, spatial, social, and situational factors—into a unified representation to enhance recommendation accuracy by modeling their complex interactions and dependencies.

161

Publications

10.2K

Citations

552

Authors

233

Institutions

Publications per year

2017–2026

121

Authors

552

  1. PB
    Peter Brusilovsky

    University of Pittsburgh, United States

    4 publications · 670 citations

    4

    h-index

  2. TK
    Tsvi Kuflik

    University of Haifa, Israel

    4 publications · 670 citations

    4

    h-index

  3. IC
    Iván Cantador

    Universidad Autónoma de Madrid, Spain

    4 publications · 670 citations

    4

    h-index

  4. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    3 publications · 137 citations

    3

    h-index

  5. CC
    Christopher Choy

    Stanford University, United States

    2 publications · 1.1K citations

    2

    h-index

1–5 of 552

Venues

  1. Expert Systems with Applications

    8 publications · 273 citations

    10

    h-index

  2. arXiv (Cornell University)

    Repository · 10 publications · 545 citations

    10

    h-index

  3. Information Fusion

    6 publications · 401 citations

    6

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 1.2K citations

    5

    h-index

  5. IEEE Access

    4 publications · 105 citations

    4

    h-index

1–5