Using Interest and Transition Models to Predict Visitor Locations in Museums

Museums offer vast amounts of information, but a visitor’s receptivity and time are typically limited — providing the visitor with the challenge of selecting the (subjectively) interesting exhibits to view within the time available. Mobile, context-aware computer systems offer the opportunity to improve a visitor’s experience by recommending exhibits of interest, and personalising the delivered content. A first step in this process is the prediction of a visitor’s activities and interests. In this paper we study non-intrusive, adaptive user modelling techniques that include consideration of the physical constraints of the exhibition layout. We present two collaborative models for predicting a visitor’s locations in a museum, and an ensemble model that combines their predictions. These models were trained and tested on a small dataset of museum visits. Our results are encouraging, with the ensemble model yielding the best performance overall.