Abstract
Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals, …) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation.
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References
Li, J., Ren, P., Chen, Z., Ren, Z., Ma, J.: Neural attentive session-based recommendation. arXiv e-prints, arXiv:1711.04725, November 2017
Dien, T.T., Thanh-Hai, N., Thai-Nghe, N.: An approach for learning resource recommendation using deep matrix factorization. J. Inf. Telecommun. (2022). https://doi.org/10.1080/24751839.2022.2058250
Sarma, D., Mittra, T., Hossain, M.S.: Personalized book recommendation system using machine learning algorithm. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 12(1) (2021). https://doi.org/10.14569/IJACSA.2021.0120126
Chandak, M., Girase, S., Mukhopadhyay, D.: Introducing hybrid technique for optimization of book recommender system. Procedia Comput. Sci. 45, 23–31 (2015). https://doi.org/10.1016/j.procs.2015.03.075
Alharthi, H., Inkpen, D., Szpakowicz, S.: A survey of book recommender systems. J. Intell. Inf. Syst. 51(1), 139–160 (2017). https://doi.org/10.1007/s10844-017-0489-9
Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. ACM Comput. Surv. 54, 7 (2021). https://doi.org/10.1145/3465401. Article 154, 38 pages
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv (2015)
de Souza Pereira Moreira, G., Ferreira, F., da Cunha, A.M.: News session-based recommendations using deep neural networks. In: Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems. ACM (2018). https://doi.org/10.1145/3270323.3270328
Ludewig, M., Jannach, D.: Evaluation of session-based recommendation algorithms. User Model. User Adap. Inter. 28(4–5), 331–390 (2018). https://doi.org/10.1007/s11257-018-9209-6
Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM (2018)
Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys 2017), pp. 130–137. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3109859.3109896
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Thai-Nghe, N., Sang, P.H. (2022). A Session-Based Recommender System for Learning Resources. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_51
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DOI: https://doi.org/10.1007/978-981-19-8069-5_51
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