A Museum Visitors Classification Based On Behavioral and Demographic Features

Abstract

This paper describes an exploratory study that attempts to classify museum visitors by taking into consideration indoor behavior and demographic features. We discuss different approaches of using such data for improving the user experience in the museum. Moreover, we try to explain user's behavior by creating different user groups using a novel data set. Our findings indicate that knowing user age, education and her museum visits frequency, together with the current visit signals (total standing time and listening to a mobile guide time) can be used for visitors classification that might be useful in designing new intelligent user interfaces that can improve the visitor's indoor experience.