Course Recommendation Model Based on Heterogeneous Graph Embedding and Session Interaction

doi:10.19678/j.issn.1000-3428.0067554

Abstract

Abstract:

The network formed by the online education platform is characterized by a large amount of data, rich entity types, and complex relationships. On the one hand, online education is being popularized, but on the other hand, online courses are facing the problems of low utilization, low completion, and high dropout rates. Personalized course recommendations are conducive to improving students' enthusiasm for learning. Among these, whether courses can be successfully completed is an important factor that students consider when selecting courses. Considering this, this study proposes a personalized course recommendation model based on the prediction of learning completion. This approach models the students' course learning session graph, and generates their learning status representations according to their course learning sequence and the completion of the course. Simultaneously, considering the influence of online learning environment factors on courses, a heterogeneous graph of online course learning is constructed, and a graph neural network is used to generate the embedding of course nodes in the graph. Thereafter, the course embeddings are fused with the students' learning status representation and the embedding of courses through an interactive mechanism to predict their degree of completion of the next course they will take. Finally, the courses are sorted according to the degree of recommendation completion. The experimental results on three large-scale online course learning datasets, namely CNPC, HMXPC, and Scholat, demonstrate that the model can effectively improve the accuracy of recommendations, and has significantly improved both the NDCG and MRR metrics compared to the baseline model optimal results. When K of the evaluation index is 5, NDCG@5 is improved by 21.08%, 17.73%, and 5.41%, respectively, and MRR@5 is improved by 25.66%, 31.59%, and 26.96%, respectively.

Key words: heterogeneous graph, session interaction, course recommendation, graph representation learning, graph neural network

摘要：

大规模在线教育平台所形成的网络具有数据量大、实体类型丰富、关系复杂等特性。一方面, 在线教育正在被大力普及, 而另一方面, 在线课程却面临低使用率、低完成度及高辍学率的问题。个性化的课程推荐有利于提高学习者的学习积极性, 其中, 课程能否顺利合格完成是学习者在选课时所考虑的重要因素。鉴于此, 提出一种基于学习完成度预测的个性化课程推荐模型。对学生的课程学习会话图进行建模, 根据学生的课程学习顺序以及课程的完成情况, 生成学生的学习状态表征; 同时考虑在线学习环境因素对课程的影响, 构建在线课程学习异质图, 采用图神经网络生成异质图中课程节点的嵌入; 然后通过交互机制融合学习状态表征和课程嵌入, 预测学生下一门将学课程的完成度, 根据完成度排序从而实现课程推荐。在CNPC、HMXPC和Scholat 3个大规模在线课程学习数据集上的实验结果表明, 该模型能有效提升推荐的准确度, 在归一化折损累计增益(NDCG)和平均倒数排名(MRR) 2个指标上相较于基线模型最优结果均有显著提升, 评估指标K值取5时, 其NDCG@5指标在3个数据集上分别提升21.08%、17.73%和5.41%, MRR@5指标在3个数据集上分别提升25.66%、31.59%和26.96%。

关键词: 异质图, 会话交互, 课程推荐, 图表征学习, 图神经网络

Zhengyang WU, Guangtao ZHANG, Li HUANG, Yong TANG. Course Recommendation Model Based on Heterogeneous Graph Embedding and Session Interaction[J]. Computer Engineering, 2024, 50(4): 95-103.

吴正洋, 张广涛, 黄立, 汤庸. 基于异质图嵌入和会话交互的课程推荐模型[J]. 计算机工程, 2024, 50(4): 95-103.

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Figures/Tables 8

Fig.1 Session of the course learning

Fig.2 The overall framework of SHRec model

Fig.3 Example of heterogeneous graph of CNPC dataset

Fig.4 The variation of NDCG@5 with embedding size

Fig.5 The variation of MRR@5 with embedding size

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