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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/5242
Title: Data-driven study: augmenting predication accuracy of recommendations in social learning platforms
Authors: Fazeli, Soude
Drachsler, Hendrik
Peter, Sloep
Keywords: Open Discovery Space
recommender system
social
learning
accuracy
Issue Date: 21-Nov-2013
Abstract: This study aims to develop a recommender system for a social learning platform to be provided by EU FP7 Open Discovery Space (ODS) project by taking into account social data of users to make recommendations. In this paper, we investigate which recommender algorithm can best fits social learning platforms like ODS platform. We conducted an experiment to test a set of different classical collaborative filtering algorithms on representative educational datasets similar to the future ODS dataset, as well as on the MovieLens dataset as a reference for studies on recommender systems. In addition to the classical collaborative filtering algorithms, we evaluated a graph-based recommender approach called T-index. We compare performance of the used algorithms in terms of F1 score. We also show how T-index approach can provide a balanced distribution of users’ degree centrality.
Description: Fazeli, S., Drachsler, H., & Sloep, P. B. (2013, 7-8 November). Data-driven study: augmenting predication accuracy of recommendations in social learning platforms. Presented in the 25th Benelux Conference on Artificial Intelligence (BNAIC 2013), Delft, The Netherlands.
URI: http://hdl.handle.net/1820/5242
Appears in Collections:2. LN: Presentations

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