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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, J., Hao, B., Chen, B., Li, C., Chen, H., Sun, J.: Hierarchical reinforcement learning for course recommendation in MOOCs. In: The 33rd AAAI Conference on Artificial Intelligence (2019). https://doi.org/10.1609/aaai.v33i01.3301435
Xie, S.-T., He, Z.-B., Chen, Q., Chen, R.-X., Kong, Q.-Z., Song, C.-Y.: Predicting learning behavior using log data in blended teaching. Sci. Program. 2021, 1–14 (2021). https://doi.org/10.1155/2021/4327896
Zhang, H., Song, Z., Sun, M., Tang, J., Wang, X., Sun, J.: Smart jump: automated navigation suggestion for videos in MOOCs. In: International World Wide Web Conference Committee (2017). https://doi.org/10.1145/3041021.3054166
Liu, J., Fu, L., Wang, X., Tang, F., Chen, G.: Joint recommendations in multilayer mobile social networks. IEEE Trans. Mob. Comput. 19, 2358–2373 (2020). https://doi.org/10.1109/TMC.2019.2923665
Wang, H., Zhang, F., Hou, M., Xie, X., Guo, M., Liu, Q.: SHINE: signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining, February 2018, pp. 592–600 (2018)
Xie, R., Qiu, Z., Rao, J., et al.: Internal and contextual attention network for cold-start multi-channel matching in recommendation. In: IJCAI 2020, pp. 2732–2738 (2020)
Matrix, N.F.: SVD Recommendation model based on positive and negative feedback matrix (2015)
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 1–22 (2012). https://doi.org/10.1145/2168752.2168771
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: 26th International World Wide Web Conference, WWW 2017 (2017)
He, X., Chua, T.S.: Neural factorization machines for sparse predictive analytics. In: International Conference on Research and Development in Information Retrieval (2017). https://doi.org/10.1145/3077136.3080777
Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: ACM International Conference Proceeding Series (2016). https://doi.org/10.1145/2988450.2988454
Qi, G.L.: The research advances of knowledge graph. Technol. Intell. Eng. (2017).https://doi.org/10.3772/j.issn.2095-915x.2017.01.002
Qiao, L.: Knowledge graph construction techniques. J. Comput. Res. Develop. 53(3), 582–600 (2016)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August 2016, pp. 353–362 (2016)
Wang, H., Zhang, F., Xie, X., Guo, M.: DKN: deep knowledge-aware network for news recommendation, pp. 1835–1844. International World Wide Web Conference Committee (2018). https://doi.org/10.1145/3178876.3186175
Wang, H., et al.: RippleNet: propagating user preferences on the knowledge graph for recommender systems. In: International Conference on Information and Knowledge Management, pp. 417–426 (2018). https://doi.org/10.1145/3269206.3271739
Wang, H., Zhang, F., Zhao, M., Li, W., Xie, X., Guo, M.: Multi-task feature learning for knowledge graph enhanced recommendation, pp. 2000–2010. International World Wide Web Conference Committee (2019). https://doi.org/10.1145/3308558.3313411
Yu, J., et al.: MOOCCube: a large-scale data repository for NLP applications in MOOCs. In: Annual Conference of the Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.acl-main.285
Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: World Wide Web Conference (2019). https://doi.org/10.1145/3308558.3313417
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.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-03948-5_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-03947-8
Online ISBN: 978-3-031-03948-5
eBook Packages: Computer ScienceComputer Science (R0)
