Sep 5, 2024 · This paper proposes a long-term knowledge tracing model to capture the dynamic interconnections among students, exercises, and skills.
Sep 7, 2024 · Heterogeneous graph neural networks (HGNNs) have demonstrated promising capabilities in addressing various problems defined on heterogeneous ...
Dynamic heterogeneous graph contrastive networks for knowledge tracing. https://doi.org/10.1016/j.asoc.2024.112194 ·. Journal: Applied Soft Computing, 2024, p ...
然而，由于知识的异质性和认知演变序列的不完整性，这是一项具有挑战性的任务。本文提出了一个基于动态强化异构图对比网络的长期知识追踪框架--KT-Deeper，以预测学生对 ...
Experimental results confirm that KT-Deeper exhibits superior performance compared to existing cutting-edge techniques, showcasing its promising accuracy and ...
A heterogeneous graph [21,23] is a graph with multiple types of vertices and edges, which can naturally describe complex relations and rich semantics in many ...
Missing: contrastive | Show results with:contrastive
Heterogeneous graph neural network (HGNN) is a popular technique for modeling and analyzing heteroge- neous graphs. Most existing HGNN-based approaches are ...
Mar 2, 2023 · To move this idea forward, we enhance our hetero- geneous graph contrastive learning with meta networks to allow the personalized knowledge ...
May 7, 2023 · In this paper, we propose a novel graph neural network model–node signature based Temporal Heterogeneous Graph Attention Network, termed as THGAT, for learning ...
Feb 27, 2023 · In this paper, we study the problem of heterogeneous graph-enhanced relational learning for recommendation.
Missing: tracing. | Show results with:tracing.
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