Using collaborative models to adaptively predict visitor locations in museums
Fabian Bohnert, Ingrid Zukerman, Shlomo Berkovsky, Timothy Baldwin, Liz Sonenberg, W Nejdl (ed.), J Kay (ed.), P Pu (ed.), E Herder (ed.)
ADAPTIVE HYPERMEDIA AND ADAPTIVE WEB-BASED SYSTEMS | SPRINGER-VERLAG BERLIN | Published : 2008
The vast amounts of information presented in museums can be overwhelming to a visitor, whose receptivity and time are typically limited. Hence, s/he might have difficulties selecting interesting exhibits to view within the available time. Mobile, context-aware guides offer the opportunity to improve a visitor's experience by recommending exhibits of interest, and personalising the delivered content. The first step in this recommendation process is the accurate prediction of a visitor's activities and preferences. In this paper, we present two adaptive collaborative models for predicting a visitor's next locations in a museum, and an ensemble model that combines their predictions. Our experim..View full abstract
Awarded by Australian Research Council
This research was supported in part by grant DP0770931 from the Australian Research Council. The authors thank Enes Makalic for his assistance with ensemble models. Thanks also go to CarolynMeehan and her team fromMuseum Victoria for fruitful discussions, their support of this research, and the dataset.