Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks
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Published:2023-05-22
Issue:3
Volume:17
Page:1-27
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ISSN:1559-1131
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Container-title:ACM Transactions on the Web
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language:en
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Short-container-title:ACM Trans. Web
Author:
Gong Jibing1ORCID, Wan Yao2ORCID, Liu Ye3ORCID, Li Xuewen1ORCID, Zhao Yi1ORCID, Wang Cheng1ORCID, Lin Yuting1ORCID, Fang Xiaohan1ORCID, Feng Wenzheng4ORCID, Zhang Jingyi5ORCID, Tang Jie4ORCID
Affiliation:
1. Yanshan University, China 2. Huazhong University of Science and Technology, China 3. Salesforce Research, USA 4. Tsinghua University, China 5. Beihang University, China
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, termedHinCRec-RL, forConceptRecommendation in MOOCs, which is based onHeterogeneousInformationNetworks andReinforcementLearning. 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 aheterogeneous 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 compared with several state-of-the-art models.
Funder
National Key R&D Program of China Hebei Natural Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Reference99 articles.
1. Charu C. Aggarwal et al. 2016. Recommender Systems. Vol. 1. Springer. 2. Integrating blockchain with artificial intelligence for privacy-preserving recommender systems;Bosri Rabeya;IEEE Transactions on Network Science and Engineering,2021 3. Xiaoyan Cai, Junwei Han, and Libin Yang. 2018. Generative adversarial network based heterogeneous bibliographic network representation for personalized citation recommendation. In Thirty-Second AAAI Conference on Artificial Intelligence. 4. Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, Jianxin Li, and Philip S. Yu. 2021. Knowledge-preserving incremental social event detection via heterogeneous GNNs. In Proceedings of the Web Conference 2021 (WWW’21). Association for Computing Machinery, New York, NY, USA, 3383–3395. 5. Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, and Jie Tang. 2019. Representation learning for attributed multiplex heterogeneous network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4–8, 2019, Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, and George Karypis (Eds.). ACM, 1358–1368.
Cited by
2 articles.
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