Sequential User Behavior Modeling
Sequential User Behavior Modeling is a computational framework that captures the temporal progression of user interactions within a funnel by representing each stage as a sequence of dependent actions, distinguishing it from static or aggregate behavioral models through its focus on order, timing, and transitional patterns in user journeys.
92
Publications
12.1K
Citations
344
Authors
121
Institutions
Publications per year
2017–2026
71
Parents
92
Neural Attentive Session-based Recommendation
Jing Li, Pengjie Ren, Zhumin Chen et al. · 2017 · 1.5K citations
Sequential Recommendation with User Memory Networks
Xu Chen, Hongteng Xu, Yongfeng Zhang et al. · 2018 · 508 citations · Full text
Translation-based Recommendation
Ruining He, Wang-Cheng Kang, Julian McAuley · 2017 · 429 citations · Full text
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121

Alibaba Group (China)
Hangzhou, China
12 h-index
47 publications
5.7K citations
University of California San Diego
San Diego, United States
5 h-index
12 publications
3K citations

Macquarie University
Sydney, Australia
4 h-index
15 publications
2.9K citations
University of Technology Sydney
Sydney, Australia
4 h-index
8 publications
1.7K citations
University of Science and Technology of China
Hefei, China
4 h-index
12 publications
584 citations

Renmin University of China
Beijing, China
4 h-index
11 publications
604 citations

Shanghai Jiao Tong University
Shanghai, China
4 h-index
18 publications
1.5K citations
Georgia Institute of Technology
Atlanta, United States
3 h-index
5 publications
1.6K citations
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Repository · 7 publications · 264 citations
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TKDE · 4 publications · 210 citations
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4 publications · 129 citations
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Conference · 2 publications · 138 citations
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Conference · 2 publications · 296 citations
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