Machine Learning For Personalization
Machine Learning for Personalization is a computational methodology that employs machine learning algorithms to dynamically model and adapt user-specific preferences, behaviors, and needs within digital environments, distinguishing it from general machine learning approaches by its focus on individualized prediction, recommendation, and interface customization in contexts such as personalized learning environments and web personalization.
256
Publications
11.6K
Citations
844
Authors
368
Institutions
Publications per year
2017–2026
163
256
Machine learning algorithms review
Qingyang Ling · Applied and Computational Engineering · 2023 · 467 citations · Full text
Artificial intelligence in recommender systems
Qian Zhang, Jie Lü, Yaochu Jin · Complex & Intelligent Systems · 2020 · 428 citations · Full text
Three Approaches for Personalization with Applications to Federated Learning
Yishay Mansour, Mehryar Mohri, Jae Hun Ro et al. · arXiv (Cornell University) · 2020 · 322 citations · Full text
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368
Carnegie Mellon University
Pittsburgh, United States
5 h-index
9 publications
171 citations

University of Electronic Science and Technology of China
Chengdu, China
4 h-index
10 publications
720 citations
Sathyabama Institute of Science and Technology
Chennai, India
3 h-index
8 publications
238 citations

Mohammed V University
Rabat, Morocco
3 h-index
6 publications
170 citations
KU Leuven
Leuven, Belgium
3 h-index
5 publications
337 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
3 h-index
13 publications
223 citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
7 publications
117 citations

Tel Aviv University
Tel Aviv, Israel
3 h-index
6 publications
340 citations
1–8 of 368
Repository · 6 publications · 400 citations
6
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6 publications · 676 citations
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6 publications · 368 citations
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4 publications · 90 citations
4
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4 publications · 139 citations
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