Trust-Based Collaborative Filtering

k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful algorithm supporting recommender systems, attempts to relieve the problem of information overload by generating predicted ratings for items users have not expressed their opinions about; to do so, each predicted rating is computed based on ratings given by like-minded individuals. Like-mindedness, or similarity-based recommendation, is the cause of a variety of problems that plague recommender systems. An alternative view of the problem, based on trust, offers the potential to address many of the previous limiations in CF. In this work we present a varation of kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one another by evaluating the utility of the rating information they receive. This method redefines the way CF is performed, and while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms the basic similarity-based methods in terms of prediction accuracy.