Abstract
To handle the limitations of collaborative filtering-based recommender systems, knowledge graphs are getting attention as side information. However, there are several problems to apply the existing KG-based methods to the course recommendations of MOOCs. We propose KPCR, a framework for Knowledge graph enhanced Personalized Course Recommendation. In KPCR, internal information of MOOCs and an external knowledge base are integrated through user and course related keywords. In addition, we add the level embedding module that predicts the level of students and courses. Through the experiments with the real-world datasets, we demonstrate that our knowledge graph boosts recommendation performance as side information. The results also show that the two auxiliary modules improve the recommendation performance. In addition, we evaluate the effectiveness of KPCR through the satisfaction survey of users of the real-world MOOCs platform.
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques).
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References
Alraimi, K.M., Zo, H., Ciganek, A.P.: Understanding the MOOCs continuance: the role of openness and reputation. Comput. Educ. 80, 28–38 (2015)
Bollacker, K., Tufts, P., Pierce, T., Cook, R.: A platform for scalable, collaborative, structured information integration. In: International Workshop on Information Integration on the Web (IIWeb 2007), pp. 22–27 (2007)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Guo, Q., et al.: A survey on knowledge graph-based recommender systems. IEEE Trans. Knowl. Data Eng. (2020)
Hu, B., Shi, C., Zhao, W.X., Yu, P.S.: Leveraging meta-path based context for top-N recommendation with a neural co-attention model. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1531–1540 (2018)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Jdidou, Y., Khaldi, M.: Increasing the profitability of students in MOOCs using recommendation systems. Int. J. Knowl. Soc. Res. (IJKSR) 7(4), 75–85 (2016)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Liang, T.P., Lai, H.J., Ku, Y.C.: Personalized content recommendation and user satisfaction: theoretical synthesis and empirical findings. J. Manag. Inf. Syst. 23(3), 45–70 (2006)
Loyd, E., Prante, M., Messinger, L.: Evaluation of Charlotte-Mecklenburg schools personalized learning initiative-year 2 (2017)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)
Parasuraman, A., Zeithaml, V.A., Berry, L.: SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. J. Retail. 64(1), 12–40 (1988)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web 8(3), 489–508 (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)
Rodriguez, M., Posse, C., Zhang, E.: Multiple objective optimization in recommender systems. In: Proceedings of the sixth ACM Conference on Recommender Systems, pp. 11–18 (2012)
Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2007)
Sun, Z., et al.: Research commentary on recommendations with side information: a survey and research directions. Electron. Commer. Res. Appl. 37, 100879 (2019)
Symeonidis, P., Malakoudis, D.: Multi-modal matrix factorization with side information for recommending massive open online courses. Expert Syst. Appl. 118, 261–271 (2019)
Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 950–958 (2019)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)
Xiaoyan, Z., Jie, B.: Research on MOOC system based on bipartite graph context collaborative filtering algorithm. In: Proceedings of the 2019 8th International Conference on Software and Computer Applications, pp. 154–158 (2019)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 353–362 (2016)
Zhang, H., Huang, T., Lv, Z., Liu, S., Yang, H.: MOOCRC: a highly accurate resource recommendation model for use in MOOC environments. Mob. Netw. Appl. 24(1), 34–46 (2019)
Zhang, J., Hao, B., Chen, B., Li, C., Chen, H., Sun, J.: Hierarchical reinforcement learning for course recommendation in MOOCs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 435–442 (2019)
Zheng, S., Rosson, M.B., Shih, P.C., Carroll, J.M.: Understanding student motivation, behaviors and perceptions in MOOCs. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 1882–1895 (2015)
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Jung, H., Jang, Y., Kim, S., Kim, H. (2022). KPCR: Knowledge Graph Enhanced Personalized Course Recommendation. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_60
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