Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

• URL: http://arxiv.org/abs/2203.11011v3
• Date: Tue, 9 May 2023 13:01:25 GMT
• Title: Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks
• Authors: Jibing Gong, Yao Wan, Ye Liu, Xuewen Li, Yi Zhao, Cheng Wang, Yuting Lin, Xiaohan Fang, Wenzheng Feng, Jingyi Zhang, Jie Tang
• Abstract summary: Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. We propose a novel approach, termed HinCRec-RL, for Concept Recommendation in MOOCs, which is based on Heterogeneous Information Networks and Reinforcement Learning.
• Score: 22.827250316693803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem of concept recommendation in this paper, which can be viewed as recommending knowledge to users in a fine-grained way. We put forward a novel approach, termed HinCRec-RL, for Concept Recommendation in MOOCs, which is based on Heterogeneous Information Networks and Reinforcement Learning. In particular, we propose to shape the problem of concept recommendation within a reinforcement learning framework to characterize the dynamic interaction between users and knowledge concepts in MOOCs. Furthermore, we propose to form the interactions among users, courses, videos, and concepts into a heterogeneous information network (HIN) to learn the semantic user representations better. We then employ an attentional graph neural network to represent the users in the HIN, based on meta-paths. Extensive experiments are conducted on a real-world dataset collected from a Chinese MOOC platform, XuetangX, to validate the efficacy of our proposed HinCRec-RL. Experimental results and analysis demonstrate that our proposed HinCRec-RL performs well when comparing with several state-of-the-art models.

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