Abstract
Nowadays, it is challenging for college students or lifelong education learners to choose the courses they need under the constant growth of massive online course resources. Therefore the recommendation systems are used to meet their personalized interests. In the scenario of course recommendation, traditional collaborative filtering (CF) is not applicable because of the sparsity of user-item interactions and the cold start problem. Learned from MKR, MKCR is proposed to enhance online courses sources recommendation when the interaction between students and courses is extremely sparse. MKCR is an end-to-end framework that utilizes a knowledge graph embedding task to assist recommendation tasks. The experiment data partially come from the MOOC platform of Chinese universities. The results show MKCR is better performance than other methods in the experiments.
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References
Wang, H., Zhang, F., Zhao, M., Li, W., **e, X., Guo, M.: Multi-task feature learning for knowledge graph enhanced recommendation. In: Proceedings of the World Wide Web Conference, WWW 2019, 2000–2010 (2019)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: International World Wide Web Conferences Steering Committee, pp. 173–182 (2017)
Liu, J., Fu, L., Wang, X., Tang, F., Chen, G.: Joint recommendations in multilayer mobile social networks. IEEE Trans. Mob. Comput. 19(10), 2358–2373 (2020)
Wang, H., Zhang, F., Hou, M., **e, 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, pp. 592–600 (2018)
Sun, Y., Yuan, N.J., **e, X., McDonald, K., Zhang, R.: Collaborative intent prediction with real-time contextual data. In: ACM Transactions on Information Systems (2017)
Cheng, H.T., Koc, L., Harmsen, J., et al.: Wide & Deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM (2016)
Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., **e, X., Guo, M.: RippleNet: propagating user preferences on the knowledge graph for recommender systems. In: International Conference on Information and Knowledge Management Proceedings, pp. 417–426 (2018)
Wang, H., et al.: RippleNet: propagating user preferences on the knowledge graph for recommender systems, pp. 417–426. ACM (2018)
Wen, Y., Kang, S., Zeng, Q., Duan, H., Chen, X., Li, W.: Session based recommendation with GNN and time-aware memory network, mobile information systems, vol. 2022, Article ID 1879367, 12 pages (2022). doi: https://doi.org/10.1155/2022/1879367
Li, J., Xu, Z., Tang, Y., Zhao, B., Tian, H.: Deep Hybrid Knowledge Graph Embedding for Top-N Recommendation. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds.) WISA 2020. LNCS, vol. 12432, pp. 59–70. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60029-7_6
Acknowledgement
This work was supported in part by the Distinguished Teachers Training Plan Program of Shandong University of Science and Technology (MS20211105), in part by the Teaching Reform Research Project of the Teaching Steering Committee of Electronic Information Specialty in Higher Education and Universities of the Ministry of Education, in part by the Special Project of China Association of Higher Education, in part by the Education and Teaching Research Project of Shandong Province, in part by the Taishan Scholar Program of Shandong Province, in part by the University-Industry Collaborative Education Program (201902316015, 202102402001), and in part by the Open Fund of the National Virtual Simulation Experimental Teaching Center for Coal Mine Safety Mining (SDUST 2019).
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Chen, X., Sun, Y., Zhou, T., Wen, Y., Zhang, F., Zeng, Q. (2022). Recommending Online Course Resources Based on Knowledge Graph. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_51
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DOI: https://doi.org/10.1007/978-3-031-20309-1_51
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