Matrix Factorization For Cold-start

Matrix Factorization for Cold-Start is a recommendation technique that extends traditional matrix factorization by incorporating auxiliary information or structural constraints to enable prediction for users or items with no historical interaction data.

89

Publications

8.5K

Citations

276

Authors

137

Institutions

Publications per year

2017–2026

45

Authors

276

  1. SK
    Samuel Kaski

    Aalto University, Finland

    3 publications · 117 citations

    3

    h-index

  2. BC
    Bee-Chung Chen

    Yahoo (United States), United States

    3 publications · 380 citations

    3

    h-index

  3. DA
    Deepak Agarwal

    Indiana University – Purdue University Indianapolis, United States

    3 publications · 380 citations

    3

    h-index

  4. CC
    Chenwei Cai

    University of California San Diego, United States

    2 publications · 60 citations

    2

    h-index

  5. HW
    Hao Wang

    Norwegian University of Science and Technology, Norway

    2 publications · 38 citations

    2

    h-index

1–5 of 276

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 749 citations

    6

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 138 citations

    5

    h-index

  3. IEEE Access

    5 publications · 148 citations

    5

    h-index

  4. Expert Systems with Applications

    4 publications · 1.1K citations

    4

    h-index

  5. Conference on Recommender Systems

    Conference · 3 publications · 157 citations

    3

    h-index

1–5