Towards Trajectory-Based Recommendations in Museums: Evaluation of Strategies Using Mixed Synthetic and Real Data

Abstract Recommendation systems, which suggest items that are of potential interest to the user (e.g., regarding which books to read, which movies to watch, etc.) have grown in popularity due to the ever-increasing amount of data available, that can lead to significant user’s overload. In particular, in recent years, extensive research has focused on the so-called Context-Aware Recommender Systems (CARS), which exploit context data to offer more relevant recommendations. In this paper, we study this problem with a use case scenario: recommending items to observe in a museum. We propose a trajectory-based and user-based collaborative filtering approach, that considers context data such as the location of the user and his/her trajectory to offer personalized recommendations. Besides, we exploit DataGenCARS, a dataset synthetic generator designed to construct datasets for the evaluation of context-aware recommendation systems, to build a mixed scenario based on both real and synthetic data. The experimental results show the advantages of the proposed approach and the usefulness of DataGenCARS for practical evaluation with a real use-case scenario.