Abstract
The present paper proposes an e-learning system that combines popularity and collaborative filtering techniques to recommend pedagogical resources. A recommender system helps users get a correct and personalized decision by applying several recommendation methods such as content-based, collaborative filtering, and other hybrid approaches. However, predicting a relevant resource with a specific context, like pedagogical content, becomes a challenge. In our work, we propose a model to ameliorate the traditional collaborative filtering technique by (i) using the Singular Value Decomposition (SVD) to tackle the problem of scalability and data sparsity; (ii) extracting the most popular resources that the user does not interact with before to resolve the cold start problem; and (iii) combining the results of popularity and SVD factorization methods to improve the recommendation accuracy that evaluated by applying the recall, precision and f1-score of each approach. The comparison shows that the obtained results exhibit an encouraging performance of our model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adomavicius, B.G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 634–749 (2005)
Ansari, M.H., Moradi, M., NikRah, O., Kambakhsh, K.M.: CodERS: a hybrid recommender system for an e-learning system. In: 2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS), pp. 1–5. IEEE (2016)
Javed, U., Shaukat, K., Hameed, I.A., Iqbal, F., Alam, T.M., Luo, S.: A review of content-based and context-based recommendation systems. Int. J. Emerg. Technol. Learn. (iJET) 16(3), 274–306 (2021)
Rajendran, D.P.D., Sundarraj, R.P.: Using topic models with browsing history in hybrid collaborative filtering recommender system: experiments with user ratings. Int. J. Inf. Manag. Data Insights 1(2), 100027 (2021)
Zhao, Q., Harper, F.M., Adomavicius,G., Konstan, J.A.: Explicit or implicit feedback? Engagement or satisfaction: a field experiment on machine-learning-based recommender systems. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 1331–1340 (2018)
Walek, B., Fojtik, V.: A hybrid recommender system for recommending relevant movies using an expert system. Expert Syst. Appl. 158, 113452 (2020)
Fazeli, S., Loni, B., Drachsler, H., Sloep, P.: Which recommender system can best fit social learning platforms? In: Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P.J. (eds.) EC-TEL 2014. LNCS, vol. 8719, pp. 84–97. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11200-8_7
Guan, X., Li, C., Guan, Y.: Matrix factorization with rating completion: an enhanced SVD model for collaborative filtering recommender systems. IEEE Access 5, 27668–27678 (2017)
Yuan, X., Han, L., Qian, S., Xu, G., Yan, H.: Singular value decomposition based recommendation using imputed data. Knowl.-Based Syst. 163, 485–494 (2019)
Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_1
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Application of dimensionality reduction in recommender system—a case study. In: ACM WebKDD 2000 (Web-mining for Ecommerce Workshop) (2000)
Kumar, R., Verma, B.K., Rastogi, S.S.: Social popularity based SVD++ recommender system. Int. J. Comput. Appl. 87(14) (2014)
Chen, W., Niu, Z., Zhao, X., Li, Y.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2012). https://doi.org/10.1007/s11280-012-0187-z
Wang, X., Zhang, Y., Yu, S., Liu, X., Yuan, Y., Wang, F.: E-learning recommendation framework based on deep learning. In: IEEE International Conference on Systems 2017, pp. 455–460. IEEE (2017)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_12
Valcarce, D., Bellogín, A., Parapar, J., Castells, P.: Assessing ranking metrics in top-N recommendation. Inf. Retr. J. 23(4), 411–448 (2020). https://doi.org/10.1007/s10791-020-09377-x
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mediani, Y., Gharzouli, M., Mediani, C. (2022). A Hybrid Recommender System for Pedagogical Resources. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-02447-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-02447-4_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-02446-7
Online ISBN: 978-3-031-02447-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)