Recommendation in museums: paths, sequences, and group satisfaction maximization

This work addresses the problem of generating and then recommending an artworks sequence for a group of visitors within a museum. Differently from a recommender system for an e-commerce application, the problem, here, is trying to maximize the satisfaction of the proposed recommendations, while taking into account an items' ordering that satisfies each group member during the sequence and the artworks locations in the museum. Moreover, since many visitors may not be able to visit every artwork, the recommender system should provide suggestions while satisfying temporal visit constraints. The problem formulation is discussed together with the characteristics of a feasible solution. An exact search algorithm from the literature is used to efficiently solve the problem and to define the prerequisites for the recommender system. Finally, we evaluate a prototype implementation with both an offline analysis and a pilot study in a simulated museum environment.