A Social Recommender System using T-index Approach and Graph Search Mechanism

The software provides a recommender system, which make recommendations for users in social learning platforms like the one for Open Discovery Space (ODS) projects. The recommender uses user interactions data like rating, bookmarking, commenting, etc. The application has been developed using Java and MySQL. The software is available under GNU Lesser General Public License (LGPL3)

Socio-semantic Networks of Research Publications in the Learning Analytics Community

Fazeli, S., Drachsler, H., & Sloep, P. B. (2013). Socio-semantic Networks of Research Publications in the Learning Analytics Community. In M. d'Aquin, S. Dietze, H. Drachsler, E. Herder, & D. Taibi (Eds.), Linked data challenge, Learning Analytic and Knowledge (LAK13) (pp. 6-10). Vol. 974, Leuven, Belgium.In this paper, we present network visualizations and an analysis of publications data from the LAK (Learning Analytics and Knowledge) in 2011 and 2012, and the special edition on Learning and Knowledge Analytics in Journal of Educational Technology and Society (JETS) in 2012.NELLL, FP7 EU Open Discovery Space (ODS

Data-driven study: augmenting predication accuracy of recommendations in social learning platforms

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.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.EU FP7 Open Discovery Spac

A trust-based social recommender for teachers

Fazeli, S., Drachsler, H., Brouns, F., & Sloep, P. B. (2012). A trust-based social recommender for teachers. In N. Manouselis, H. Drachsler, K. Verbert, & O. C. Santos (Eds.), 2nd Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2012) in conjunction with the 7th European Conference on Technology Enhanced Learning (EC-TEL 2012) (pp. 49-60). September, 18-19, 2012, Saarbrücken, Germany.Online communities and networked learning provide teachers with social learning opportunities to interact and collaborate with others in order to develop their personal and professional skills. In this paper, Learning Networks are presented as an open infrastructure to provide teachers with such learning opportunities. However, with the large number of learning resources produced everyday, teachers need to find out what are the most suitable resources for them. In this paper, recommender systems are introduced as a potential solution to address this issue. Unfortunately, most of the educational recommender systems cannot make accurate recommendations due to the sparsity of the educational datasets. To overcome this problem, we propose a research approach that describes how one may take advantage of the social data which are obtained from monitoring the activities of teachers while they are using our social recommender.NELLL, Open Discovery Space (ODS

Implicit vs. Explicit Trust in Social Matrix Factorization

This poster presented at the RecSys2014, Silicon Valley, US Oct. 6th-10th, 2014.NELLL, EU FP7 LAC

Supporting Users of Open Online Courses with Recommendations: an Algorithmic Study

Almost all studies on course recommenders in online platforms target closed online platforms that belong to a University or other provider. Recently, a demand has developed that targets open platforms. Such platforms lack rich user profiles with content metadata. Instead they log user interactions. We report on how user interactions and activities tracked in open online learning platforms may generate recommendations. We use data from the OpenU open online learning platform in use by the Open University of the Netherlands to investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. It appears that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system outperforms the classical approaches on prediction accuracy of recommendations in terms of recall.FP7 EU LAC