Hierarchical Session Modeling
Hierarchical Session Modeling is a session-based recommendation framework that structures user interactions into multiple levels of abstraction to capture both short-term and long-term patterns within and across sessions, distinguishing itself from flat or non-hierarchical approaches by explicitly modeling the temporal and structural dependencies in a layered manner.
8
Publications
173
Citations
27
Authors
14
Institutions
Publications per year
2017–2026
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Parents
8
Learning a Hierarchical Intent Model for Next-Item Recommendation
Nengjun Zhu, Jian Cao, Xinjiang Lu et al. · ACM Transactions on Information Systems · 2021 · 20 citations
Contextual Re-Ranking with Behavior Aware Transformers
Chen Qu, Chenyan Xiong, Yizhe Zhang et al. · 2020 · 19 citations
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Rutgers, The State University of New Jersey
New Brunswick, United States
1 h-index
1 publications
20 citations
University of Washington
Seattle, United States
1 h-index
1 publications
27 citations

Sun Yat-sen University
Guangzhou, China
1 h-index
3 publications
39 citations

University of Massachusetts Amherst
Amherst Center, United States
1 h-index
2 publications
38 citations
Baidu (China)
Beijing, China
1 h-index
1 publications
20 citations

Beijing University of Posts and Telecommunications
Beijing, China
1 h-index
4 publications
108 citations
Microsoft (United States)
Redmond, United States
1 h-index
4 publications
76 citations
Seattle University
Seattle, United States
1 h-index
1 publications
27 citations
1–8 of 14
2 publications · 38 citations
2
h-index
1 publications · 41 citations
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1 publications · 27 citations
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1 publications · 21 citations
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Conference · 1 publications · 14 citations
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