Abstract
The MOOC platforms usually recommend online course resources to users among a large number of courses on platform whose users are almost college students or students by selves. However, the entire course as the recommendation results may ignore student interests in some specific knowledge points, and so may reduce student learning interests. The recommendation not courses but online learning videos of course are researched. The connection between a single learning video and other entities is considered to construct learning video knowledge graph. Based on the end-to-end deep learning framework, a convolutional neural network KGCN-LV is applied in learning videos recommendation, which is integrated learning video knowledge graph. Experimental results on public data set MOOCCube show that the learning video recommendation method based on knowledge graph is up to 5% better than the traditional collaborative filtering method. The recommendation performance is effectively improved, and the recommendation effect is interpretable.
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Acknowledgement
This work is partially supported by the Open Fund of the National Virtual Simulation Experimental Teaching Center for Coal Mine Safety Mining (SDUST 2019), the 2018 Postgraduate Tutors’ Guidance Ability Improvement Project of Shandong Province (SDYY18084), the Teaching Reform Research Project of the Teaching Steering Committee of Electronic Information Specialty in Higher Education and Universities of the Ministry of Education, the Special Project of China Association of Higher Education, the Education and Teaching Research Project of Shandong Province, and Excellent Teaching Team Construction Project of Shandong University of Science and Technology.
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Chen, X., Sun, Y., Zhou, T., Zeng, Q., Qi, H. (2022). A Method on Online Learning Video Recommendation Method Based on Knowledge Graph. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_34
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DOI: https://doi.org/10.1007/978-3-031-03948-5_34
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