Transformer-based Sequential Recommendation

Transformer-Based Sequential Recommendation is a sequential recommendation framework that employs the transformer architecture to model user behavior sequences by leveraging self-attention mechanisms for capturing long-range dependencies and complex sequential patterns in user-item interactions.

48

Publications

3.7K

Citations

245

Authors

86

Institutions

Publications per year

2017–2026

48

Authors

245

  1. XZ
    Xiangyu Zhao

    City University of Hong Kong, Hong Kong SAR China

    4 publications · 178 citations

    4

    h-index

  2. ZL
    Zhiwei Liu

    University of Illinois Chicago, United States

    4 publications · 331 citations

    4

    h-index

  3. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    4 publications · 331 citations

    4

    h-index

  4. WO
    Wenwu Ou

    Alibaba Group (China), China

    3 publications · 699 citations

    3

    h-index

  5. ZF
    Ziwei Fan

    University of Illinois Chicago, United States

    3 publications · 248 citations

    3

    h-index

1–5 of 245

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 409 citations

    4

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 173 citations

    3

    h-index

  3. 2

    h-index

  4. 2

    h-index

  5. ACM Transactions on Information Systems

    3 publications · 69 citations

    2

    h-index

1–5