Context-Aware Collaborative Filtering System: Predicting the User’s Preference in the Ubiquitous Computing Environment

  • Annie Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3479)


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Ardissono, L., Goy, A., Giovanna, P.: INTRIGUE: Personalized recommendation of tourist attractions for desktop and handset devices. Applied Artificial Intelligence 17, 687–714 (2003)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)zbMATHCrossRefGoogle Scholar
  8. 8.
    Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining Collaborative Filtering Recommendations. In: Computer Supported Cooperative Work, pp. 241–250 (2000)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Schmidt, A., Beigl, M., Gellersen, H.-W.: There is more to context than location. Computers and Graphics 23, 893–901 (1999)CrossRefGoogle Scholar
  12. 12.,

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Annie Chen
    • 1
  1. 1.IBM Zurich Research LaboratoryRüschlikonSwitzerland

Personalised recommendations