Applying ant-colony concepts to trust-based recommender systems

Collaborative filtering is a recommender technique that recommends items to an individual user based on the item ratings provided by similar users. However, current systems often do not acquire sufficient ratings to be able to generate recommendations. Trust-based recommender systems have been proposed that use additional trust values in generating recommendations. In this paper, we propose a trust-based ant recommender with two main improvements. First, we achieve better selection of higher-quality raters by our proposed trust-calculation method and an improved pheromone-update mechanism. Second, we can improve the prediction step by converting raters' ratings into a target user's perspective view and considering the influence level of each rater on the active user. The Epinions dataset was used in experiments comparing the proposed method with the ALT-BAR method. The evaluation showed that the proposed method provides better results in term of both accuracy and coverage.