Artworks Sequences Recommendations for Groups in Museums

In an Internet of Things vision of smarts museums, recommendation systems based on collaborative filtering approaches can be exploited in the context of providing personalized artworks tours. In this work, we address 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 location in the museum. In this work, we present a general framework to address such problems and evaluate a prototype implementation with both an offline analysis and a pilot study in a simulated museum environment.