Context-aware Sequential Recommendation

Context-Aware Sequential Recommendation is a sequential recommendation approach that incorporates situational, environmental, or user-state information—such as time, location, device, or social context—into the modeling of user behavior sequences to improve the accuracy and personalization of next-item predictions.

40

Publications

2.8K

Citations

149

Authors

75

Institutions

Publications per year

2017–2026

35

Authors

149

  1. LW
    Liang Wang

    Chinese Academy of Sciences, China

    3 publications · 272 citations

    3

    h-index

  2. SW
    Shu Wu

    Chinese Academy of Sciences, China

    3 publications · 272 citations

    3

    h-index

  3. VS
    Victor S. Sheng

    Texas Tech University, United States

    2 publications · 619 citations

    2

    h-index

  4. YL
    Yanchi Liu

    Rutgers, The State University of New Jersey, United States

    2 publications · 619 citations

    2

    h-index

  5. QL
    Qiang Liu

    National University of Defense Technology, China

    2 publications · 212 citations

    2

    h-index

1–5 of 149

Venues

  1. Information Sciences

    4 publications · 221 citations

    4

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 372 citations

    3

    h-index

  3. 2

    h-index

  4. 1

    h-index

  5. IEEE Access

    1 publications · 16 citations

    1

    h-index

1–5