Recommendation systems have become a popular approach for accessing relevant products and information. Existing approaches for movie recommendation systems are insufficient, because they do not provide transparency to the users through enabling them to view and edit their profiles. In addition, negative feedback, which is an important clue for the recommender, is not taken into account. In this paper we concentrate on the ideas of automatically generating user profiles from the user's item preferences, and enabling users to view and edit their profiles to get satisfaction. In addition, taking negative feedback for specific values is examined and discussed, which is observed to produce more accurate recommendations. The system also provides the explanations for the produced recommendations and allows users to modify their profile accordingly and see their modifications' effects on the results directly. initial experimental results demonstrate that the system produces accurate recommendations and gets user trust and satisfaction with the transparency and explanation facility.