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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Personalised Pathway Prediction
verfasst von : Fabian Bohnert, Ingrid Zukerman
Erschienen in: User Modeling, Adaptation, and Personalization
Verlag: Springer Berlin Heidelberg
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This paper proposes a personalised frequency-based model for predicting a user’s pathway through a physical space, based on non-intrusive observations of users’ previous movements. Specifically, our approach estimates a user’s transition probabilities between discrete locations utilising personalised transition frequency counts, which in turn are estimated from the movements of other similar users. Our evaluation with a real-world dataset from the museum domain shows that our approach performs at least as well as a non-personalised frequency-based baseline, while attaining a higher predictive accuracy than a model based on the spatial layout of the physical museum space.