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
Parents
95
Sequential Recommendation via Stochastic Self-Attention
Ziwei Fan, Zhiwei Liu, Yu Wang et al. · Proceedings of the ACM Web Conference 2022 · 2022 · 184 citations
Warm Up Cold-start Advertisements
Feiyang Pan, Shuokai Li, Xiang Ao et al. · 2019 · 167 citations
Vincent Leroy, B. Barla Cambazoğlu, Francesco Bonchi · 2010 · 166 citations · Full text
Computing, Data Science, Forecasting +12
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365
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125
Tongji University
Shanghai, China
4 h-index
15 publications
628 citations
Guangzhou Institute of Energy Conversion
Guangzhou, China
4 h-index
13 publications
507 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
6 publications
229 citations

Shanghai Jiao Tong University
Shanghai, China
3 h-index
5 publications
1.6K citations

University of Waterloo
Waterloo, Canada
2 h-index
6 publications
684 citations

Beijing University of Technology
Beijing, China
2 h-index
8 publications
188 citations
Ford Motor Company (United States)
Dearborn, United States
2 h-index
5 publications
51 citations
Tsinghua University
Beijing, China
2 h-index
3 publications
261 citations
1–8 of 125
14 publications · 278 citations
11
h-index
8 publications · 412 citations
8
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3 publications · 137 citations
3
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3 publications · 99 citations
3
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Conference · 2 publications · 211 citations
2
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1–5