Recommender systems are widely used to provide e-commerce users appropriate items. Collaborative filtering is one of the most successful recommender approaches which recommends items to a given user based on the opinions of his/her like-minded neighbors. However, the user-item ratings matrix, which is used as an input to the recommendation algorithm, is often highly sparse, leading to unreliable predictions. Recent studies demonstrated that information from social networks such as trust statements can be employed to improve accuracy of recommendations. However, there are not explicit trust relationships between most of users in many e-commerce applications. In this manuscript, we propose a method to identify implicit trust statements by applying a specific reliability measure. The Pareto dominance and confidence concepts are used to identify the most prominent users of which opinions are employed in the recommendation process. The proposed recommendation algorithm shows significant improvements in terms of accuracy and coverage measures as compared to the state-of-the-art recommenders.
