Self-supervised Sequential Recommendation

Self-Supervised Sequential Recommendation is a sequential recommendation framework that leverages self-supervised learning to model user-item interaction sequences by generating supervisory signals from the raw data itself, thereby improving sequential pattern learning without relying on explicit auxiliary information or external annotations.

24

Publications

2.3K

Citations

105

Authors

31

Institutions

Publications per year

2017–2026

24

Authors

105

  1. ZL
    Zhiwei Liu

    University of Illinois Chicago, United States

    5 publications · 545 citations

    5

    h-index

  2. CH
    Chao Huang

    University of Hong Kong, Hong Kong SAR China

    4 publications · 168 citations

    4

    h-index

  3. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    4 publications · 181 citations

    4

    h-index

  4. AK
    Alexandros Karatzoglou

    Telefonica Research and Development, Spain

    3 publications · 279 citations

    3

    h-index

  5. YC
    Yongjun Chen

    Salesforce (United States), United States

    3 publications · 469 citations

    3

    h-index

1–5 of 105

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 126 citations

    4

    h-index

  2. 2

    h-index

  3. ACM Transactions on Information Systems

    2 publications · 90 citations

    2

    h-index

  4. 2

    h-index

  5. 1

    h-index

1–5