Going to the cinema or watching television are social activities that generally take place in groups. In these cases, a recommender system for ephemeral groups of users is more suitable than (well-studied) recommender systems for individuals. In this paper we present a recommendation system for groups of users that go to the cinema. The system uses the Slope One algorithm for computing individual predictions and the Multiplicative Utilitarian Strategy as a model to make a recommendation to an entire group. We show how we solved all practical aspects of the system; including its architecture and a mobile application for the service, the lack of user data (ramp-up and cold-start problems), the scaling fit of the group model strategy, and other improvements in order to reduce the response time. Finally, we validate the performance of the system with a set of experiments with 57 ephemeral groups.
An Extended Hybrid Recommender System Based on Association Rules Mining in Discussion Groups
