Transformer-based Recommendation

Transformer-Based Recommendation is a recommendation approach that employs the transformer architecture—characterized by self-attention mechanisms—to model sequential user-item interactions for generating personalized recommendations.

84

Publications

4.6K

Citations

341

Authors

117

Institutions

Publications per year

2017–2026

84

Authors

341

  1. WO
    Wenwu Ou

    Alibaba Group (China), China

    5 publications · 900 citations

    5

    h-index

  2. XZ
    Xiangyu Zhao

    City University of Hong Kong, Hong Kong SAR China

    4 publications · 178 citations

    4

    h-index

  3. YZ
    Yongfeng Zhang

    Rutgers, The State University of New Jersey, United States

    3 publications · 319 citations

    3

    h-index

  4. WW
    Wanyu Wang

    City University of Hong Kong, Hong Kong SAR China

    3 publications · 161 citations

    3

    h-index

  5. FS
    Fei Sun

    Alibaba Group (China), China

    3 publications · 477 citations

    3

    h-index

1–5 of 341

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 364 citations

    6

    h-index

  2. 5

    h-index

  3. ACM Transactions on Information Systems

    3 publications · 75 citations

    5

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 137 citations

    4

    h-index

  5. Proceedings of the ACM Web Conference 2022

    Conference · 2 publications · 526 citations

    2

    h-index

1–5