Making decision on which movie to watch is nowadays not an easy process for majority of people. Many people may decide to watch a movie based on the genre attribute of a movie, while for the others, the director can be the attribute that drives them to watch a movie. Hence, people may have di↵erent features, taken into account, when deciding which movie to watch. Recommender Systems can help people in making decision by allowing them to enter the attribute(s), that is the most important to them, and filter the movie catalog accordingly. In this paper we try to investigate the process that results in choosing a movie to watch by people. Hence we present an ongoing work that will ultimately lead to building a dataset (called PoliMovie) that will contain the preferences of users not only on movies but also on attributes of the movies, such as genre, director, and cast., that users selected as the most important attributes when choosing a movie to watch. We report some preliminary results based on the preferences collected from about 400 users, which confirm the di↵erence and complexity of decision making process for di↵erent users.