Abstract
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.
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References
Bohnert, F., Zukerman, I.: Non-intrusive personalisation of the museum experience. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 197–209. Springer, Heidelberg (2009)
Bohnert, F., Zukerman, I., Berkovsky, S., Baldwin, T., Sonenberg, L.: Using interest and transition models to predict visitor locations in museums. AI Communications 21(2-3), 195–202 (2008)
Stock, O., Zancanaro, M., Busetta, P., Callaway, C., Krüger, A., Kruppa, M., Kuflik, T., Not, E., Rocchi, C.: Adaptive, intelligent presentation of information for the museum visitor in PEACH. User Modeling and User-Adapted Interaction 18(3), 257–304 (2007)
Wang, Y., Aroyo, L., Stash, N., Sambeek, R., Schuurmans, Y., Schreiber, G., Gorgels, P.: Cultivating personalized museum tours online and on-site. Interdisciplinary Science Reviews 34(2), 141–156 (2009)
Cantino, A.S., Roberts, D.L., Isbell, C.L.: Autonomous nondeterministic tour guides: Improving quality of experience with TTD-MDPs. In: Proc. of the 6th Intl. Joint Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS-07), pp. 91–93 (2007)
Han, S.J., Cho, S.B.: Predicting user’s movement with a combination of self-organizing map and Markov model. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 884–893. Springer, Heidelberg (2006)
Krumm, J.: A Markov model for driver turn prediction. In: Proc. of the Society of Automotive Engineers (SAE) 2008 World Congress (2008) Paper 2008-01-0195
Krumm, J., Horvitz, E.: Predestination: Inferring destinations from partial trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243–260. Springer, Heidelberg (2006)
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Bohnert, F., Zukerman, I. (2010). Personalised Pathway Prediction. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_33
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DOI: https://doi.org/10.1007/978-3-642-13470-8_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13469-2
Online ISBN: 978-3-642-13470-8
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