SUMMARY
These days, due to growing the e-commerce sites, access to information about items is easier than past. But because of huge amount of information, we need new filtering techniques to find interested information faster and more accurate. Therefore Recommender Systems (RS) introduced for solving this problem. Although several recommender approaches have proposed, Collaborative Filtering (CF) approaches are the most successful ones. These approaches use historical behaviors of users for making recommendation. Next generation of CF, called Trust-based CF, use social relations and activities for measuring trust between users. One important step in these approaches is measuring the similarity between users, which affect recommendation results. Therefore variety methods for this reason have been proposed. In this paper, we will review and categorize the measurement methods. We will also analyze the methods to identify their characteristics, benefits and drawbacks.