Collaborative filtering is a general technique for recommender systems, aiming to provide users with personalized recommendations. However, it suffers from two severe issues known as data sparsity and cold start. In this research, we present two different solutions to ameliorate these issues. First, we propose a trust-aware recommender system to incorporate the ratings of trusted neighbors and to form a more complete rating profile for the active users. Second, we also propose a novel Bayesian similarity measure by taking into account both the direction and length of rating profiles. Both research work shows promising empirical results based on real-world data sets. Finally, we outline the future research on trust-based clustering methods to further alleviate the concerned problems.