User Cold-start Recommendation

User Cold-Start Recommendation is a recommendation approach that addresses the challenge of generating accurate recommendations for new users with little or no historical interaction data by leveraging auxiliary information such as demographic attributes, social connections, or explicit preferences, thereby distinguishing it from general recommendation methods reliant on extensive user behavior logs.

372

Publications

21.3K

Citations

1.3K

Authors

431

Institutions

Publications per year

2017–2026

215

Authors

1.3K

  1. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    6 publications · 159 citations

    6

    h-index

  2. JL
    Jingjing Li

    University of Electronic Science and Technology of China, China

    5 publications · 351 citations

    5

    h-index

  3. IC
    Iván Cantador

    Universidad Autónoma de Madrid, Spain

    4 publications · 143 citations

    4

    h-index

  4. TZ
    Tao Zhou

    University of Electronic Science and Technology of China, China

    4 publications · 382 citations

    4

    h-index

  5. FC
    Fu-Lai Chung

    Hong Kong Polytechnic University, Hong Kong SAR China

    4 publications · 147 citations

    4

    h-index

1–5 of 1.3K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 14 publications · 915 citations

    13

    h-index

  2. IEEE Access

    13 publications · 434 citations

    13

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 471 citations

    9

    h-index

  4. arXiv (Cornell University)

    Repository · 9 publications · 248 citations

    9

    h-index

  5. Expert Systems with Applications

    8 publications · 1.6K citations

    8

    h-index

1–5