  The aim of the recommender systems is to provide relevant and potentially
interesting information to each user. This is fulfilled by utilizing the
already recorded tendencies of similar users or detecting items similar to
interested items of the user. Challenges such as data sparsity and cold start
problem are addressed in recent studies. Utilizing social information not only
enhances the prediction accuracy but also tackles the data sparseness
challenges. In this paper, we investigate the impact of using direct and
indirect trust information in a memory-based collaborative filtering
recommender system. An adaptive similarity measure is proposed and the
contribution of social information is tuned using two learning schemes, greedy
and gradient-based optimization. The results of the proposed method are
compared with state-of-the-art memory-based and model-based CF approaches on
two real-world datasets, Epinions and FilmTrust. The experiments show that our
method is quite effective in designing an accurate and comprehensive
recommender system.
