The exponential growth of social networks has made establishing a trusted relationship increasingly important. Recommender systems can play an important role in assessing a user's trustworthiness. Such systems are designed to offer recommendations of trustworthiness when establishing connections among social network members, where the system rates members by inferring their degrees of trust. In this work, we developed a recommender system that provides recommendations about trusted social network members. We compared the time complexity and the accuracy of the following four adapted algorithms and a new proposed algorithm: