Online Sequential Recommendation

Online Sequential Recommendation is a sequential recommendation paradigm that operates in real-time by incrementally updating user representations and generating recommendations from streaming user-item interaction data without reprocessing the entire historical sequence.

55

Publications

2.3K

Citations

215

Authors

67

Institutions

Publications per year

2017–2026

48

Variants

Streaming Sequential Recommendation

Authors

215

  1. FS
    Fei Sun

    Alibaba Group (China), China

    3 publications · 182 citations

    3

    h-index

  2. JW
    Ji-Rong Wen

    Renmin University of China, China

    3 publications · 95 citations

    3

    h-index

  3. WX
    Wayne Xin Zhao

    Renmin University of China, China

    3 publications · 95 citations

    3

    h-index

  4. JT
    Jie Tang

    Tsinghua University, China

    2 publications · 117 citations

    2

    h-index

  5. FL
    Fuyu Lv

    Alibaba Group (China), China

    2 publications · 165 citations

    2

    h-index

1–5 of 215

Venues

  1. Information Processing & Management

    4 publications · 109 citations

    4

    h-index

  2. Expert Systems with Applications

    4 publications · 82 citations

    4

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 138 citations

    3

    h-index

  4. Knowledge-Based Systems

    3 publications · 61 citations

    3

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 154 citations

    2

    h-index

1–5