Graph-based Sequential Recommendation
Graph-Based Sequential Recommendation is a sequential recommendation framework that models user-item interactions and item transitions as a graph structure to capture complex, high-order dependencies and semantic relations among items for more accurate next-item prediction.
43
Publications
2.3K
Citations
180
Authors
77
Institutions
Publications per year
2017–2026
42
Parents
43
Next-item Recommendation with Sequential Hypergraphs
Jianling Wang, Kaize Ding, Liangjie Hong et al. · 2020 · 281 citations
Dynamic Graph Neural Networks for Sequential Recommendation
Mengqi Zhang, Shu Wu, Xueli Yu et al. · IEEE Transactions on Knowledge and Data Engineering · 2022 · 204 citations
Unified Point-of-Interest Recommendation with Temporal Interval Assessment
Yanchi Liu, Chuanren Liu, Bin Liu et al. · 2016 · 124 citations
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Tsinghua University
Beijing, China
4 h-index
8 publications
244 citations
Shandong University
Jinan, China
3 h-index
11 publications
703 citations

Shanghai Jiao Tong University
Shanghai, China
3 h-index
6 publications
502 citations

Shandong Normal University
Jinan, China
3 h-index
10 publications
580 citations

The University of Queensland
Brisbane, Australia
3 h-index
6 publications
350 citations
Tencent (China)
Shenzhen, China
3 h-index
11 publications
365 citations

Griffith University
Brisbane, Australia
3 h-index
4 publications
184 citations

East China Normal University
Shanghai, China
3 h-index
6 publications
190 citations
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TKDE · 3 publications · 333 citations
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5 publications · 147 citations
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3 publications · 112 citations
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2 publications · 93 citations
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Conference · 1 publications · 144 citations
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