Cold Start Prediction

Cold Start Prediction is a computational framework designed to estimate the performance or behavior of a system, model, or process during its initial operational phase when little to no historical data is available, distinguishing it from general predictive methods by specifically addressing the challenges of data scarcity at inception.

95

Publications

4.9K

Citations

365

Authors

125

Institutions

Publications per year

2017–2026

49

Authors

365

  1. KJ
    Kui Jiao

    Tianjin University, China

    4 publications · 273 citations

    4

    h-index

  2. AM
    Ahmed Mohmed Dafalla

    Guangzhou Institute of Energy Conversion, China

    4 publications · 152 citations

    4

    h-index

  3. LW
    Lin Wei

    Guangzhou Institute of Energy Conversion, China

    3 publications · 126 citations

    3

    h-index

  4. FJ
    Fangming Jiang

    Guangzhou Institute of Energy Conversion, China

    3 publications · 240 citations

    3

    h-index

  5. SW
    Steven Wooldridge

    Ford Motor Company (United States), United States

    2 publications · 53 citations

    2

    h-index

1–5 of 365

Venues

  1. 11

    h-index

  2. International Journal of Hydrogen Energy

    8 publications · 412 citations

    8

    h-index

  3. Knowledge-Based Systems

    3 publications · 137 citations

    3

    h-index

  4. IEEE Access

    3 publications · 99 citations

    3

    h-index

  5. Proceedings of the ACM Web Conference 2022

    Conference · 2 publications · 211 citations

    2

    h-index

1–5