Attention-based Recommendation

Attention-Based Recommendation is a recommendation approach that employs attention mechanisms to selectively emphasize influential features or items in user preference modeling, thereby distinguishing itself from other content-based or collaborative filtering methods through its dynamic weighting of input signals.

125

Publications

6.7K

Citations

505

Authors

183

Institutions

Publications per year

2017–2026

113

Authors

505

  1. FW
    Fangzhao Wu

    Tsinghua University, China

    4 publications · 715 citations

    4

    h-index

  2. XX
    Xing Xie

    Microsoft Research Asia (China), China

    3 publications · 718 citations

    3

    h-index

  3. YH
    Yongfeng Huang

    Tsinghua University, China

    3 publications · 700 citations

    3

    h-index

  4. YJ
    Yunliang Jiang

    Huzhou University, China

    3 publications · 114 citations

    3

    h-index

  5. JL
    Jungang Lou

    Huzhou University, China

    3 publications · 114 citations

    3

    h-index

1–5 of 505

Venues

  1. Expert Systems with Applications

    8 publications · 231 citations

    8

    h-index

  2. Knowledge-Based Systems

    7 publications · 191 citations

    7

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 299 citations

    6

    h-index

  4. IEEE Access

    6 publications · 111 citations

    6

    h-index

  5. Neurocomputing

    6 publications · 240 citations

    6

    h-index

1–5