Attention-based Feature Interactions

Attention-Based Feature Interactions is a machine learning method that selectively emphasizes relevant feature combinations through attention mechanisms, distinguishing itself from other feature interaction approaches by dynamically weighting the importance of interacting features based on contextual input.

62

Publications

4.2K

Citations

233

Authors

110

Institutions

Publications per year

2017–2026

49

Authors

233

  1. FW
    Furu Wei

    Microsoft Research Asia (China), China

    2 publications · 197 citations

    2

    h-index

  2. ZW
    Zhengyao Wen

    Wuyi University, China

    2 publications · 291 citations

    2

    h-index

  3. JX
    Jun Xiao

    Zhejiang University, China

    2 publications · 988 citations

    2

    h-index

  4. XH
    Xiangnan He

    University of Science and Technology of China, China

    2 publications · 988 citations

    2

    h-index

  5. GX
    Ge Xu

    East China University of Science and Technology, China

    2 publications · 291 citations

    2

    h-index

1–5 of 233

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 377 citations

    7

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 368 citations

    4

    h-index

  3. IEEE Access

    2 publications · 39 citations

    2

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 88 citations

    2

    h-index

  5. Perception & Psychophysics

    2 publications · 40 citations

    2

    h-index

1–5