Feature-based Recommendation

Feature-Based Recommendation is a knowledge-based recommendation approach that generates personalized suggestions by modeling item and user characteristics as explicit features, distinguishing itself through the systematic integration of feature-level attributes into the inference and decision-making process.

137

Publications

5.3K

Citations

494

Authors

199

Institutions

Publications per year

2017–2026

77

Authors

494

  1. LW
    Le Wu

    Hefei University of Technology, China

    2 publications · 117 citations

    2

    h-index

  2. YD
    Yucong Duan

    Hainan University, China

    2 publications · 26 citations

    2

    h-index

  3. BL
    Bin Li

    Yangzhou University, China

    2 publications · 26 citations

    2

    h-index

  4. JZ
    Jie Zhang

    Nanyang Technological University, Singapore

    2 publications · 54 citations

    2

    h-index

  5. RJ
    Robert J. Walker

    University of Calgary, Canada

    2 publications · 33 citations

    2

    h-index

1–5 of 494

Venues

  1. IEEE Access

    7 publications · 251 citations

    7

    h-index

  2. Expert Systems with Applications

    5 publications · 274 citations

    5

    h-index

  3. Procedia Computer Science

    4 publications · 144 citations

    4

    h-index

  4. arXiv (Cornell University)

    Repository · 3 publications · 66 citations

    3

    h-index

  5. 2

    h-index

1–5