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Title: Which Recommender System Can Best Fit Social Learning Platforms?
Authors: Fazeli, Soude
Loni, Babak
Drachsler, Hendrik
Sloep, Peter
Keywords: recommender system
collaborative filtering
Issue Date: 9-Dec-2014
Citation: Fazeli, S., Loni, B., Drachsler, H., Sloep, P. (2014, 16-19 September). Which recommender system can best fit social learning platforms? In C. Rensing, S. de Freitas, T. Ley, & P. Muñoz-Merino (Eds.), Open Learning and Teaching in Educational Communities. Proceedings of the 9th European Conference on Technology Enhanced Learning (EC-TEL2014), Lecture Notes in Computer Science 8719 (pp. 84-97). Graz, Austria: Springer International Publishing.
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.
Appears in Collections:1. TELI Publications, books and conference papers
1. LN: Publications and Preprints

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