Abstract
In this paper we present a context-aware collaborative filtering system that predicts a user’s preference in different context situations based on past experiences. We extend collaborative filtering techniques so that what other like-minded users have done in similar context can be used to predict a user’s preference towards an activity in the current context. Such a system can help predict the user’s behavior in different situations without the user actively defining it. For example, it could recommend activities customized for Bob for the given weather, location, and traveling companion(s), based on what other people like Bob have done in similar context.
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References
Wang, X.H., Gu, T., Zhang, D.Q., Pung, H.K.: Ontology Based Context Modeling and Reasoning using OWL. In: Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, pp. 18–22 (2004)
van Setten, M., Pokraev, S., Koolwaaij, J.: Context-aware recommendations in the mobile tourist application COMPASS. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 235–244. Springer, Heidelberg (2004)
Ardissono, L., Goy, A., Giovanna, P.: INTRIGUE: Personalized recommendation of tourist attractions for desktop and handset devices. Applied Artificial Intelligence 17, 687–714 (2003)
Cheverst, K., Davies, N., Mitchell, K., Friday, A., Efstratiou, C.: Developing a Context-aware Electronic Tourist Guide: Some Issues and Experiences. In: CHI 2000, pp. 17–24 (2000)
McCarthy, J.F.: Pocket RestaurantFinder: A Situated Recommender System for Groups. In: Workshop on Mobile Ad-Hoc Communication at the 2002 ACM Conference on Human Factors in Computer Systems (2002)
Schafer, J.B., Konstan, J., Riedl, J.: Recommender Systems in E-Commerce. In: Proceedings of the 1st ACM conference on Electronic commerce, pp. 158–166. ACM Press, New York (1999)
Lawrence, R.D., Almasi, G.S., Kotlyar, V., Viveros, M.S., Duri, S.S.: Personalization of Supermarket Product Recommendations. Data Mining and Knowledge Discovery 5, 11–32 (2001)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining Collaborative Filtering Recommendations. In: Computer Supported Cooperative Work, pp. 241–250 (2000)
Herlocker, J.L., Konstan, J.A., Borchers, A.l.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237. ACM Press, New York (1999)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Schmidt, A., Beigl, M., Gellersen, H.-W.: There is more to context than location. Computers and Graphics 23, 893–901 (1999)
VirtualTourist.com, http://www.virtualtourist.com
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Chen, A. (2005). Context-Aware Collaborative Filtering System: Predicting the User’s Preference in the Ubiquitous Computing Environment. In: Strang, T., Linnhoff-Popien, C. (eds) Location- and Context-Awareness. LoCA 2005. Lecture Notes in Computer Science, vol 3479. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11426646_23
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DOI: https://doi.org/10.1007/11426646_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25896-4
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