Abstract
Traditional desktop search paradigm often does not fit mobile contexts. Common mobile devices provide impoverished mechanisms for text entry and small screens are able to offer only a limited set of options, therefore the users are not usually able to specify their needs. On a different note, mobile technologies have become part of the everyday life as shown by the estimate of one billion of mobile broadband subscriptions in 2011.
This paper describes an approach to make context-aware mobile interaction available in scenarios where users might be looking for categories of points of interest (POIs), such as cultural events and restaurants, through remote location-based services. Empirical evaluations shows how rich representations of user contexts has the chance to increase the relevance of the retrieved POIs.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, pp. 335–336. ACM, New York (2008), http://doi.acm.org/10.1145/1454008.1454068
Al-Masri, E., Mahmoud, Q.H.: Smartcon: a context aware service discovery and selection mechanism using artificial neural networks. Int. J. Intell. Syst. Technol. Appl. 6, 144–156 (2009), http://portal.acm.org/citation.cfm?id=1497600.1497609
Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive Mob. Comput. 6(2), 161–180 (2010)
Console, L., Torre, I., Lombardi, I., Gioria, S., Surano, V.: Personalized and adaptive services on board a car: An application for tourist information. J. Intell. Inf. Syst. 21, 249–284 (2003), http://portal.acm.org/citation.cfm?id=939823.940055
García-Crespo, A., Chamizo, J., Rivera, I., Mencke, M., Colomo-Palacios, R., Gómez-Berbís, J.M.: Speta: Social pervasive e-tourism advisor. Telemat. Inf. 26, 306–315 (2009), http://portal.acm.org/citation.cfm?id=1514442.1514676
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst. 20, 422–446 (2002), http://doi.acm.org/10.1145/582415.582418
Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Building personal maps from gps data. Annals of the New York Academy of Sciences 1093(1), 249–265 (2005)
Mokbel, M.F., Levandoski, J.J.: Toward context and preference-aware location-based services. In: MobiDE 2009: Proceedings of the Eighth ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 25–32. ACM, New York (2009)
Sohn, T., Li, K.A., Griswold, W.G., Hollan, J.D.: A diary study of mobile information needs. In: CHI 2008: Proceeding of the Twenty-sixth Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 433–442. ACM, New York (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Biancalana, C., Flamini, A., Gasparetti, F., Micarelli, A., Millevolte, S., Sansonetti, G. (2011). Enhancing Traditional Local Search Recommendations with Context-Awareness. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-22362-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22361-7
Online ISBN: 978-3-642-22362-4
eBook Packages: Computer ScienceComputer Science (R0)