基于学习事件超图的课程资源推荐方法研究
Research on Course Resource Recommendation Method Based on Learning Event Hypergraph
摘要: 学习行为建模是实现个性化推荐的关键。现有的研究方法通过序列化建模学习者的行为数据来学习其进化的偏好。然而，这些方法仍然存在着噪声关系干扰、对项目间高阶关系信息考虑不足等问题。因此，本文提出了一种基于学习事件超图的课程资源推荐方法，该方法首先利用基于超图的学习事件来表示项目间的高阶信息，并对学习者行为进行建模。然后，利用超图神经网络和带有时间位置信号的自注意力机制表示学习者的学习行为特征，并通过预测学习者下一步要交互的项目实现个性化的序列推荐。在数据集上的实验结果表明，该方法能够有效地解决学习行为建模中信息缺乏的问题，从而提高个性化推荐的质量和准确性。
Abstract: Learning behavior modeling is the key to realizing personalized recommendation. Existing research methods learn evolutionary preferences by serializing behavioral data of modeling learners. However, these methods still have some problems, such as noise interference and insufficient consideration of high-order relationship information between projects. Therefore, this paper proposes a learning event hypergraph-based course resource recommendation method. Firstly, learning events based on hypergraph are used to represent high-order information between items and model learners’ behavior. Then, hypergraph neural network and self-attention mechanism with time and position signals are used to represent learners’ learning behavior characteristics, and personalized sequence recommendation is realized by predicting the items that learners will interact with in the next step. Experimental results on data sets show that this method can effectively solve the problem of lack of information in learning behavior modeling, thus improving the quality and accuracy of personalized recommendation.
文章引用：李冬晴, 马彪. 基于学习事件超图的课程资源推荐方法研究[J]. 计算机科学与应用, 2022, 12(12): 2884-2895. https://doi.org/10.12677/CSA.2022.1212293

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