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) ha
Researchers propose MR-ConceptGCN, a multi-relational graph convolutional network approach for sequential learner modeling, combining personal knowledge graphs and pre-trained language models. The method is evaluated in an online user study with 31 participants, demonstrating benefits in accuracy, usefulness, and user satisfaction. MR-ConceptGCN enhances relation- and semantic-aware representations.
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