Session Representation Learning
Session Representation Learning is a computational framework within session-based recommendation that models user behavior sequences as variable-length sessions and captures their latent structural patterns to generate concise, fixed-dimensional vector representations distinguishing it from broader user modeling approaches by focusing solely on anonymous, short-term interaction sequences without relying on persistent user identities.
160
Publications
12.1K
Citations
559
Authors
202
Institutions
Publications per year
2017–2026
154
Parents
160
Neural Attentive Session-based Recommendation
Jing Li, Pengjie Ren, Zhumin Chen et al. · 2017 · 1.5K citations
A Simple Convolutional Generative Network for Next Item Recommendation
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis et al. · 2019 · 625 citations · Full text
Graph Contextualized Self-Attention Network for Session-based Recommendation
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu et al. · 2019 · 624 citations · Full text
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559
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202

The University of Queensland
Brisbane, Australia
8 h-index
23 publications
5K citations
Tencent (China)
Shenzhen, China
7 h-index
17 publications
1.4K citations

National University of Defense Technology
Changsha, China
7 h-index
25 publications
1.4K citations

University of Electronic Science and Technology of China
Chengdu, China
7 h-index
16 publications
838 citations
Institute of Computing Technology
Beijing, China
6 h-index
12 publications
1.5K citations
Shandong University
Jinan, China
6 h-index
14 publications
9.1K citations

Macquarie University
Sydney, Australia
6 h-index
15 publications
418 citations

Alibaba Group (China)
Hangzhou, China
5 h-index
18 publications
2.9K citations
1–8 of 202
12 publications · 345 citations
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10 publications · 292 citations
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Repository · 8 publications · 199 citations
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7 publications · 219 citations
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7 publications · 123 citations
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