The problem of choosing the right product that will best fit the consumers’ tastes and preferences also extends even in electronic commerce. However, e-commerce was able to create a technological proxy for this social filtering process that is called online Recommender Systems (RS). RS act as personalized decision guides, aiding users in decisions on matters related to personal taste. It has the potential to support and improve the quality of the decisions consumers make when searching for and selecting products and services online. However, most previous research on RS has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user’s perspective. This study identified transparency and feedback as some of the possible ways to effectively evaluate recommender systems based from the users’ perspective. Thus, the goal of this research wants to focus on examining and identifying the roles of transparency and feedback in recommender systems and how it affect the user’s attitude towards the system.