Abstract
In our daily life, we are increasingly surrounded by devices that expose us to quantities of information well behind our cognitive capabilities. To overcome this problem various authors propose mechanisms capable of selecting only the most relevant pieces of information.
In this paper, we propose an approach for information selection based on the concepts of relevance, selective attention and diversity. The idea is to select the most promising items in terms of surprise and usefulness and dismiss those that are less promising.
We illustrate our approach with an example of an application for restaurant selection and show the first results from an initial evaluation of this system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
O’Connell, N.: Interruption overload. Strategic Direction 24(10), 3–5 (2008)
Keim, D.A., Kriegel, H.P.: VisDB: Database Exploration Using Multidimensional Visualization. IEEE Computer Graphics and Applications 14(5), 40–49 (1994)
Sabbata, S., Reichenbacher, T.: A Probabilistic Model of Geographic Relevance. In: GIR 2010, Zurich, Switzerland (2010)
Burigat, S., Chitttaro, L.: Decluttering of Icons based on Aggregation in Mobile Maps. In: Map-based Mobile Services - Design, Interaction and Usability, pp. 13–32. Springer, Heidelberg (2008)
Pombinho, P., Carmo, M.B., Afonso, A.P.: Evaluation of Overcluttering Prevention Techniques for Mobile Devices. In: IV 2009 (2009)
Edwardes, A., Burghardt, D., Weibe, R.: Portrayal and Generalisation of Point Maps for Mobile Information Services. In: Map-based Mobile Services, pp. 11–30. Springer, Berlin (2005)
Driver, J.: A selective review of selective attention research from the past century. Br. J. Psychol. 92, 53–78 (2001)
Macedo, L., Reisenzein, R., Cardoso, A.: Modeling Forms of Surprise in Artificial Agents: Empirical and Theoretical Study of Surprise Functions. In: Proceedings of the 26th Annual Conference of the Cognitive Science Society (2004)
Fonseca, N.G., Rente, L., Bento, C.: Artificial Selective Attention for In-Vehicle Information Systems. In: Proceedings of the 2nd European Conference on Human Centred Design for Intelligent Transport Systems, Berlin (2010)
Gago, P., Bento, C.: A Metric for Selection of the Most Promising Rules. In: Zytkow, J., Quafafou, M. (eds.) PKDD 1998. LNCS (LNAI), vol. 1510, pp. 19–27. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fonseca, N.G., Rente, L., Bento, C. (2010). Selective Delivery of Points of Interest. In: de Ruyter, B., et al. Ambient Intelligence. AmI 2010. Lecture Notes in Computer Science, vol 6439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16917-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-16917-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16916-8
Online ISBN: 978-3-642-16917-5
eBook Packages: Computer ScienceComputer Science (R0)