CLAYTON SCHOOL OF INFORMATION TECHNOLOGY
MONASH UNIVERSITY

TECHNICAL REPORT 2008/219

Using Interest and Transition Models to Predict Visitor Locations in Museums

Fabian Bohnert, Ingrid Zukerman, Shlomo Berkovsky, Timothy Baldwin and Elizabeth Sonenberg

ABSTRACT

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.