Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks
User modeling is a critical task in a variety of personalized systems. Recognizing their effectiveness in learning from graph-structured data, Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), are increasingly employed for user modeling. However, existing approaches typically treat different relation types in a graph as homogeneous, limiting their ability to capture richer semantics and construct more informative user models. While multi-relational GNNs (MR-GNNs) have been adopted for representation learning and recommendation, their application for user modeling
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 46%Graph Neural Networks: When Relationships Are the Signal - Snowflake →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzySimilar title/name (fuzzy) · 59%tirth8205/code-review-graph →
“Fuzzy title match (0.73): “Sequential Learner Modeling Using Multi-Relational Graph Con” ≈ “tirth8205/code-review-graph””
- LinkedLinked via arxiv author · 85%Rawaa Alatrash →
“Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks”
- LinkedLinked via arxiv author · 85%Mohamed Amine Chatti →
“Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks”
- LinkedLinked via arxiv author · 85%Shizhong Yang →
“Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks”
- LinkedLinked via arxiv author · 85%Yumeng Wang →
“Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks”