Long-term User Modeling
Long-Term User Modeling is a user profiling approach in personalized search that distinguishes itself by capturing persistent user interests and behaviors over extended periods to enable accurate, context-aware personalization across diverse interactions and temporal spans.
31
Publications
2.3K
Citations
137
Authors
51
Institutions
Publications per year
2017–2026
21
Parents
31
Neural News Recommendation with Long- and Short-term User Representations
Mingxiao An, Fangzhao Wu, Chuhan Wu et al. · 2019 · 331 citations · Full text
Algorithmic Effects on the Diversity of Consumption on Spotify
Ashton Anderson, Lucas Maystre, Ian Anderson et al. · 2020 · 187 citations · Full text
Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation
Zeping Yu, Jianxun Lian, Ahmad Mahmoody et al. · 2019 · 178 citations · Full text
Chenyang Wang, Min Zhang, Weizhi Ma et al. · 2020 · 128 citations
Computing, History, Human Behavior +13
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51

Shanghai Jiao Tong University
Shanghai, China
3 h-index
9 publications
1.1K citations
Microsoft Research Asia (China)
Beijing, China
2 h-index
5 publications
1.3K citations

Zhejiang University
Hangzhou, China
2 h-index
9 publications
313 citations

University of Glasgow
Glasgow, United Kingdom
2 h-index
2 publications
62 citations
Tsinghua University
Beijing, China
2 h-index
6 publications
971 citations
Tencent (China)
Shenzhen, China
2 h-index
6 publications
256 citations

The University of Sydney
Sydney, Australia
2 h-index
5 publications
91 citations

Universitat Pompeu Fabra
Barcelona, Spain
2 h-index
6 publications
327 citations
1–8 of 51
1 publications · 44 citations
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1 publications · 115 citations
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1 publications · 13 citations
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1 publications · 12 citations
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1 publications · 22 citations
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