Cold-start Evaluation

Cold-Start Evaluation is a methodological framework within recommendation system assessment that specifically addresses the challenge of evaluating recommender systems when insufficient historical interaction data is available for new users, items, or contexts, thereby differentiating it from general evaluation approaches that assume established user-item interaction patterns.

51

Publications

3.8K

Citations

183

Authors

81

Institutions

Publications per year

2017–2026

28

Authors

183

  1. DM
    David M. Pennock

    Yahoo (United States), United States

    3 publications · 2K citations

    3

    h-index

  2. AI
    Andrew I. Schein

    University of Pennsylvania, United States

    2 publications · 1.8K citations

    2

    h-index

  3. AP
    Alexandrin Popescul

    University of Pennsylvania, United States

    2 publications · 1.8K citations

    2

    h-index

  4. LU
    Lyle Ungar

    University of Pennsylvania, United States

    2 publications · 1.8K citations

    2

    h-index

  5. ME
    Mehdi Elahi

    Free University of Bozen-Bolzano, Italy

    2 publications · 77 citations

    2

    h-index

1–5 of 183

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 39 citations

    2

    h-index

  2. Expert Systems with Applications

    2 publications · 57 citations

    2

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 11 citations

    1

    h-index

  4. Electronic Commerce Research and Applications

    1 publications · 14 citations

    1

    h-index

  5. Applied Intelligence

    1 publications · 23 citations

    1

    h-index

1–5