Cold-start Sequential Recommendation

Cold-Start Sequential Recommendation is a subclass of sequential recommendation that specifically addresses the challenge of generating accurate personalized item sequences for users or items with little to no historical interaction data by incorporating auxiliary information or transferable knowledge to mitigate data sparsity.

20

Publications

1.2K

Citations

78

Authors

36

Institutions

Publications per year

2017–2026

19

Authors

78

  1. JW
    Jinpeng Wang

    Renmin University of China, China

    2 publications · 52 citations

    2

    h-index

  2. HY
    Hongxia Yang

    Alibaba Group (China), China

    2 publications · 117 citations

    2

    h-index

  3. SW
    Shu Wu

    Chinese Academy of Sciences, China

    2 publications · 247 citations

    2

    h-index

  4. ZD
    Zhengxiao Du

    Tsinghua University, China

    2 publications · 117 citations

    2

    h-index

  5. RH
    Ruining He

    University of California San Diego, United States

    2 publications · 60 citations

    2

    h-index

1–5 of 78

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 26 citations

    2

    h-index

  2. 2

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 229 citations

    2

    h-index

  4. Expert Systems with Applications

    1 publications · 23 citations

    1

    h-index

  5. Information Systems

    1 publications · 68 citations

    1

    h-index

1–5