Abstract
This study aims to develop a recommender system for social learning platforms that combine traditional learning management systems with commercial social networks like Facebook. We therefore take into account social interactions of users to make recommendations on learning resources. We propose to make use of graph-walking methods for improving performance of the well-known baseline algorithms. We evaluate the proposed graph-based approach in terms of their F1 score, which is an effective combination of precision and recall as two fundamental metrics used in recommender systems area. The results show that the graph-based approach can help to improve performance of the baseline recommenders; particularly for rather sparse educational datasets used in this study.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vassileva, J.: Toward Social Learning Environments. Learning 1(4), 199–214 (2009)
Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., Duval, E.: Dataset-driven research for improving recommender systems for learning. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 44–53 (2011)
Golbeck, J.: Computing and applying trust in web-based social networks. University of Maryland at College Park, College Park (2005)
Massa, P., Avesani, P.: Trust-aware Recommender Systems. In: RecSys 2007 Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 17–24 (2007)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, p. 203 (2009)
Fazeli, S., Zarghami, A., Dokoohaki, N., Matskin, M.: Mechanizing social trust-aware recommenders with T-index augmented trustworthiness. In: Katsikas, S., Lopez, J., Soriano, M. (eds.) TrustBus 2010. LNCS, vol. 6264, pp. 202–213. Springer, Heidelberg (2010)
Anjorin, M., Rodenhausen, T., Domínguez García, R., Rensing, C.: Exploiting Semantic Information for Graph-Based Recommendations of Learning Resources. In: Ravenscroft, A., Lindstaedt, S., Kloos, C.D., Hernández-Leo, D. (eds.) EC-TEL 2012. LNCS, vol. 7563, pp. 9–22. Springer, Heidelberg (2012)
Manouselis, N., Drachsler, H., Verbert, K., Duval, E.: Recommender Systems for Learning, pp. 1–61. Springer, Heidelberg (2012)
Fazeli, S., Drachsler, H., Brouns, F., Sloep, P.: Towards a Social Trust-aware Recommender for Teachers. Spec. issue Recomm. Syst. Technol. Enhanc. Learn. Res. Trends Appl. (2013)
Manouselis, N., Vuorikari, R., Van Assche, F.: Collaborative recommendation of e-learning resources: an experimental investigation. J. Comput. Assist. Learn. 26(4), 227–242 (2010)
Cechinel, C., Sicilia, S., Sánchez-Alonso, M.-Á., García-Barriocanal, E.: Evaluating collaborative filtering recommendations inside large learning object repositories. Inf. Process. Manag. (2012)
Koukourikos, A., Stoitsis, G., Karampiperis, P.: Sentiment Analysis: A tool for Rating Attribution to Content in Recommender Systems. In: Manouselis, N., Drachsler, H., Verbert, K., Santos, O.C. (eds.) Proceedings of the 2nd Workshop on Recommender Systems in Technology Enhanced Learning 2012, 7th European Conference on Technology Enhanced Learning (EC-TEL 2012), pp. 61–70 (2012)
Drachsler, H., Bogers, T., Vuorikari, R., Verbert, K., Duval, E., Manouselis, N., Beham, G., Lindstaedt, S., Stern, H., Friedrich, M., Wolpers, M.: Issues and considerations regarding sharable data sets for recommender systems in technology enhanced learning. Procedia Comput. Sci. 1(2), 2849–2858 (2010)
Manouselis, N., Kyrgiazos, G., Stoitsis, G.: Revisiting the Multi-Criteria Recommender System of a Learning Portal. In: Manouselis, N., Drachsler, H., Verbert, K., Santos, O.C. (eds.) Proceedings of the 2nd Workshop on Recommender Systems in Technology Enhanced Learning 2012, 7th European Conference on Technology Enhanced Learning (EC-TEL 2012), pp. 35–48 (2012)
Thai-Nghe, N., Drumond, L., Grimberghe, A., Schmidt-Thieme, L., Krohn-Grimberghe, A.: Recommender system for predicting student performance. Procedia Comput. Sci. 1(2), 2811–2819 (2010)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian Personalized Ranking from Implicit Feedback. In: UAI 2009 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)
Rendle, S.: Context-Aware Ranking with Factorization Models. Stud. Comput. Intell. 330 (2011)
Ning, X., Karypis, G.: SLIM: Sparse Linear Methods for Top-N Recommender Systems. In: IEEE 11th International Conference on Data Mining, pp. 497–506 (2011)
Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. U.S.A. 102(16569) (2005)
Schmitz, H.-C., Scheffel, M., Friedrich, M., Jahn, M., Niemann, K., Wolpers, M.: CAMera for PLE. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 507–520. Springer, Heidelberg (2009)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Fazeli, S., Loni, B., Drachsler, H., Sloep, P. (2014). Which Recommender System Can Best Fit Social Learning Platforms?. In: Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P.J. (eds) Open Learning and Teaching in Educational Communities. EC-TEL 2014. Lecture Notes in Computer Science, vol 8719. Springer, Cham. https://doi.org/10.1007/978-3-319-11200-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-11200-8_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11199-5
Online ISBN: 978-3-319-11200-8
eBook Packages: Computer ScienceComputer Science (R0)