Journal article

Using interest and transition models to predict visitor locations in museums

Fabian Bohnert, Ingrid Zukerman, Shlomo Berkovsky, Timothy Baldwin, Liz Sonenberg

AI COMMUNICATIONS | IOS PRESS | Published : 2008

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 available time. Mobile, electronic handheld guides offer the opportunity to improve a visitor's experience by recommending exhibits of interest, and adapting the delivered content. The first step in this personalisation process is the prediction of a visitor's activities and interests. In this paper we study non-intrusive, adaptive user modelling techniques that take into account the physical constraints of the exhibition layout. We present two collaborative models for predicting..

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Grants

Awarded by Australian Research Council


Funding Acknowledgements

This research was supported in part by Discovery grant DP0770931 from the Australian Research Council. The authors thank Enes Makalic for his assistance with ensemble models. Thanks also go to Carolyn Meehan and her team from Museum Victoria for fruitful discussions and the dataset.