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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 wellknown 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.

Abstract. Online communities and networked learning provide teachers with social learning opportunities, allowing them to interact and collaborate with others in order to develop their personal and professional skills. However, with the large number of learning resources produced every day, teachers need to find out what are the most suitable ones for them. In this paper, we introduce recommender systems as a potential solution to this. The setting is the Open Discovery Space (ODS) project. Unfortunately, due to the sparsity of the educational datasets most educational recommender systems cannot make accurate recommendations. To overcome this problem, we propose to enhance a trustbased recommender algorithm with social data obtained from monitoring the activities of teachers within the ODS platform. In this article, we outline the requirements of the ODS recommender system based on experiences reported in related TEL recommender system studies. In addition, we provide empirical evidence from a survey study with stakeholders of the ODS project to support the requirements identified from a literature study. Finally, we present an agenda for further research intended to find out which recommender system should ultimately be deployed in the ODS platform.

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The growing popularity of Social Networks raises the important issue of trust. Among many systems which have realized the impact of trust, Recommender Systems have been the most influential ones. Collaborative Filtering Recommenders take advantage of trust relations between users for generating more accurate predictions. In this paper, we propose a semantic recommendation framework for creating trust relationships among all types of users with respect to different types of items, which are accessed by unique URI across heterogeneous networks and environments. We gradually build up the trust relationships between users based on the rating information from user profiles and item profiles to generate trust networks of users. For analyzing the formation of trust networks, we employ Tindex as an estimate of a user’s trustworthiness to identify and select neighbors in an effective manner. In this work, we utilize T-index to form the list of an item’s raters, called TopTrustee list for keeping the most reliable users who have already shown interest in the respective item. Thus, when a user rates an item, he/she is able to find users who can be trustworthy neighbors even though they might not be accessible within an upper bound of traversal path length. An empirical evaluation demonstrates how T-index improves the Trust Network structure by generating connections to more trustworthy users. We also show that exploiting Tindex results in better prediction accuracy and coverage of recommendations collected along few edges that connect users on a Social Network.

Abstract-Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user satisfaction should be also performed before deploying a recommender system to a real target environment. Such evaluations are laborious and complicated compared to the traditional, data-centric evaluations, though. In this study, we investigate the added value of user-centric evaluations and how user satisfaction of a recommender system is related to its performance in terms of accuracy metrics. We conduct both a data-centric evaluation and a user-centric evaluation on the same data collected from an authentic social learning platform. Our findings suggest that user-centric evaluation results are not necessarily in line with data-centric evaluation results. We conclude that the traditional evaluation of recommender systems in terms of prediction accuracy does not suffice to judge performance of recommender systems on the user side. Moreover, the user-centric evaluation provides valuable insights on how candidate algorithms perform on each of the five quality metrics: usefulness, accuracy, novelty, diversity, and serendipity of the recommendations.

Social Networks have dominated growth and popularity of the Web to an extent which has never been witnessed before. Such popularity puts forward issue of trust to the participants of Social Networks. Collaborative Filtering Recommenders have been among many systems which have begun taking full advantage of Social Trust phenomena for generating more accurate predictions. For analyzing the evolution of constructed networks of trust, we utilize Collaborative Filtering enhanced with T-index as an estimate of a user's trustworthiness to identify and select neighbors in an effective manner. Our empirical evaluation demonstrates how T-index improves the Trust Network structure by generating connections to more trustworthy users. We also show that exploiting T-index results in better prediction accuracy and coverage of recommendations collected along few edges that connect users on a network.

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