Cold-start Item Modeling

Cold-Start Item Modeling is an academic research area within item modeling that focuses on developing methods to represent and predict item properties or behaviors in recommendation systems when little to no historical interaction data is available for the item.

9

Publications

1.1K

Citations

45

Authors

21

Institutions

Publications per year

2017–2026

5

Authors

45

  1. AE
    Anne E. Boyer

    Centers for Disease Control and Prevention, United States

    1 publications · 18 citations

    1

    h-index

  2. ZZ
    Ziwei Zhu

    Texas A&M University, United States

    1 publications · 38 citations

    1

    h-index

  3. WJ
    Wei Jian

    Purdue University West Lafayette, United States

    1 publications · 714 citations

    1

    h-index

  4. OA
    Oren Anava

    Technion – Israel Institute of Technology, Israel

    1 publications · 26 citations

    1

    h-index

  5. AB
    Armelle Brun

    Laboratoire Lorrain de Recherche en Informatique et ses Applications, France

    1 publications · 18 citations

    1

    h-index

1–5 of 45

Venues

  1. Expert Systems with Applications

    2 publications · 730 citations

    2

    h-index

  2. Journal of Computational Science

    1 publications · 57 citations

    1

    h-index

  3. 1

    h-index

  4. Journal of Intelligent Information Systems

    1 publications · 18 citations

    1

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 83 citations

    1

    h-index

1–5