Elevating Prediction Accuracy in Trust-aware Collaborative Filtering Recommenders through T-index Metric and TopTrustee lists

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 T-index 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.