Multi-interest Sequential Recommendation

Multi-Interest Sequential Recommendation is a sequential recommendation approach that models users' evolving preferences by capturing multiple distinct interests within their interaction sequences, thereby distinguishing itself from traditional methods that represent users as single, unified interest vectors.

9

Publications

631

Citations

41

Authors

15

Institutions

Publications per year

2017–2026

9

Authors

41

  1. HY
    Hongxia Yang

    Alibaba Group (China), China

    2 publications · 324 citations

    2

    h-index

  2. YC
    Yukuo Cen

    Tsinghua University, China

    2 publications · 324 citations

    2

    h-index

  3. JZ
    Jianwei Zhang

    University at Albany, State University of New York, United States

    2 publications · 324 citations

    2

    h-index

  4. JT
    Jie Tang

    Tsinghua University, China

    2 publications · 324 citations

    2

    h-index

  5. CZ
    Chang Zhou

    Alibaba Group (China), China

    2 publications · 324 citations

    2

    h-index

1–5 of 41

Venues

  1. Expert Systems with Applications

    1 publications · 16 citations

    1

    h-index

  2. 1

    h-index

  3. ACM Transactions on Information Systems

    1 publications · 42 citations

    1

    h-index

  4. 1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 16 citations

    1

    h-index

1–5