Dynamic Preference Modeling
Dynamic Preference Modeling is a computational framework within context-aware recommendation that captures and adapts user preferences over time by integrating temporal dynamics and contextual influences to model how preference structures evolve.
87
Publications
5.9K
Citations
280
Authors
137
Institutions
Publications per year
2017–2026
54
Parents
87
Collaborative filtering with temporal dynamics
Yehuda Koren · 2009 · 1.2K citations
Collaborative filtering with temporal dynamics
Yehuda Koren · Communications of the ACM · 2010 · 776 citations
A Dynamic Attribute Satiation Model of Variety-Seeking Behavior
Leigh McAlister · Journal of Consumer Research · 1982 · 557 citations · Full text
Filter-enhanced MLP is All You Need for Sequential Recommendation
Kun Zhou, Hui Yu, Wayne Xin Zhao et al. · Proceedings of the ACM Web Conference 2022 · 2022 · 342 citations · Full text
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137
Tsinghua University
Beijing, China
5 h-index
12 publications
792 citations
University of California, Berkeley
Berkeley, United States
3 h-index
6 publications
251 citations
Aristotle University of Thessaloniki
Thessaloniki, Greece
3 h-index
5 publications
288 citations

Renmin University of China
Beijing, China
3 h-index
7 publications
1.1K citations

University of Alberta
Edmonton, Canada
2 h-index
4 publications
360 citations
Texas A&M University
College Station, United States
2 h-index
7 publications
107 citations

University of Glasgow
Glasgow, United Kingdom
2 h-index
4 publications
395 citations

University of Massachusetts Amherst
Amherst Center, United States
2 h-index
4 publications
96 citations
1–8 of 137
3 publications · 119 citations
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Conference · 3 publications · 88 citations
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2 publications · 31 citations
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Repository · 2 publications · 41 citations
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TKDE · 2 publications · 98 citations
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