Sequence-aware Recommendation

Sequence-Aware Recommendation is a type of recommender system approach that incorporates temporal or sequential patterns in user interactions to model the dynamic evolution of user preferences and context over time, distinguishing itself from static or context-independent recommendation methods by explicitly leveraging the order and structure of past user behavior.

242

Publications

17.4K

Citations

841

Authors

273

Institutions

Publications per year

2017–2026

197

Authors

841

  1. FS
    Fei Sun

    Alibaba Group (China), China

    5 publications · 2.8K citations

    5

    h-index

  2. MR
    Michael R. Lyu

    Chinese University of Hong Kong, Hong Kong SAR China

    5 publications · 966 citations

    5

    h-index

  3. WO
    Wenwu Ou

    Alibaba Group (China), China

    4 publications · 3K citations

    4

    h-index

  4. IK
    Irwin King

    Chinese University of Hong Kong, Hong Kong SAR China

    4 publications · 936 citations

    4

    h-index

  5. EC
    Enhong Chen

    University of Science and Technology of China, China

    4 publications · 288 citations

    4

    h-index

1–5 of 841

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 582 citations

    11

    h-index

  2. Expert Systems with Applications

    8 publications · 215 citations

    8

    h-index

  3. IEEE Access

    8 publications · 188 citations

    8

    h-index

  4. Information Sciences

    7 publications · 332 citations

    7

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 572 citations

    6

    h-index

1–5