Attention-based Sequential Recommendation

Attention-Based Sequential Recommendation is a sequential recommendation method that employs attention mechanisms to selectively model the influence of items in a user's interaction sequence by assigning varying importance weights to different historical interactions.

78

Publications

10.2K

Citations

346

Authors

122

Institutions

Publications per year

2017–2026

78

Authors

346

  1. JM
    Julian McAuley

    University of California San Diego, United States

    6 publications · 3.6K citations

    6

    h-index

  2. HY
    Hongzhi Yin

    The University of Queensland, Australia

    5 publications · 432 citations

    5

    h-index

  3. GX
    Guandong Xu

    University of Technology Sydney, Australia

    4 publications · 562 citations

    4

    h-index

  4. SH
    Sung Hun Kim

    Catholic University of Korea, South Korea

    3 publications · 209 citations

    3

    h-index

  5. SW
    Shu Wu

    Chinese Academy of Sciences, China

    3 publications · 100 citations

    3

    h-index

1–5 of 346

Venues

  1. Information Processing & Management

    4 publications · 114 citations

    4

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 332 citations

    4

    h-index

  3. Neurocomputing

    3 publications · 185 citations

    3

    h-index

  4. 3

    h-index

  5. IEEE Transactions on Multimedia

    2 publications · 61 citations

    2

    h-index

1–5