使用多关系图卷积网络的序列学习者建模
Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks
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中文总结 AI 辅助
该研究针对用户建模问题，提出MR-ConceptGCN方法，结合个人知识图谱、多关系图卷积网络和预训练语言模型，构建序列学习者模型，通过在线用户研究验证了该方法在多个用户关注方面的优势。
中文摘要 AI 辅助
用户建模在各种个性化系统中至关重要。图神经网络（GNN），特别是图卷积网络（GCN），因其在从图结构数据学习中的有效性，越来越多地用于用户建模。但现有方法将图中不同关系类型视为同质，限制了捕获丰富语义和构建信息更丰富用户模型的能力。多关系GNN（MR-GNN）虽用于表示学习和推荐，但其在用户建模中的应用尚待探索。此外，现有基于GNN的用户建模方法忽略了用户交互序列。为填补这些研究空白，本文提出MR-ConceptGCN，一种全新的完全无监督方法，专注于使用多关系GCN进行基于概念的序列学习者建模。MR-ConceptGCN有效结合个人知识图谱（PKG）、MR-GCN和预训练语言模型SBERT，以获得PKG项目增强的关系和语义感知表示。然后，利用学习者在CourseMapper中与学习材料交互时不理解的知识概念的丰富嵌入，构建一个结合长期和短期学习者交互的序列学习者模型。我们报告了一项在线用户研究（n = 31）的结果，证明了MR-ConceptGCN在包括准确性、有用性、多样性和对教育推荐系统的满意度等几个重要的以用户为中心的方面的优势。
英文摘要
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 remains unexplored. Moreover, existing GNN-based user modeling approaches ignore the user interaction sequence. To address these research gaps, in this work we propose MR-ConceptGCN, a novel fully unsupervised approach focused on concept-based sequential learner modeling using multi-relational GCNs (MR-GCNs). MR-ConceptGCN effecively combines Personal Knowledge Graphs (PKGs), MR-GCNs, and the pre-trained language model SBERT to obtain enhanced relation- and semantic-aware representations of the PKG items. The enriched embeddings of the knowledge concepts that a learner did not understand when interacting with learning materials in CourseMapper are then used to construct a sequential learner model that combines long-term and short-term learner interactions. We report the results of an online user study (n = 31), demonstrating the benefits of MR-ConceptGCN in terms of several important user-centric aspects including accuracy, usefulness, diversity, and satisfaction with an educational recommender system.
