Trust-aware recommender systems are intelligent technology applications that make use of trust information and user personal data in social networks to provide personalized recommendations. Earlier research in trust-aware systems have shown that the ability of trust-based systems to make accurate predictions coupled with their robustness from shilling attacks make them a better alternative than traditional recommender systems. In this paper we propose an approach for improving accuracy of predictions in trust-aware recommender systems. In our approach, we first reconstruct the trust network. Trust network is reconstructed by removing trust links between users having correlation coefficient below a specified threshold value. For prediction calculation we compare three different approaches based on trust and correlation. We show through experiments on real life Epinions data set that our proposed approach of reconstructing the trust network gives substantially better prediction accuracy than the original approach of using all trust statements in the network.
In management education programmes today, students face a difficult time in choosing electives as the number of electives available are many. As the range and diversity of different elective courses available for selection have increased, course recommendation systems that help students in making choices about courses have become more relevant. In this paper we extend the concept of collaborative filtering approach to develop a course recommendation system. The proposed approach provides student an accurate prediction of the grade they may get if they choose a particular course, which will be helpful when they decide on selecting elective courses, as grade is an important parameter for a student while deciding on an elective course. We experimentally evaluate the collaborative filtering approach on a real life data set and show that the proposed system is effective in terms of accuracy.
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