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Context-aware collaborative filtering system: predicting the user's preferences in ubiquitous computing
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Source Conference on Human Factors in Computing Systems archive
CHI '05 extended abstracts on Human factors in computing systems table of contents
Portland, OR, USA
SESSION: Doctoral consortium table of contents
Pages: 1110 - 1111  
Year of Publication: 2005
ISBN:1-59593-002-7
Author
Annie Chen  IBM Zurich Research Laboratory, Rüschlikon, Switzerland
Sponsors
ACM: Association for Computing Machinery
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

In this paper I propose a context-aware collaborative filtering system that can predict a user's preference in different context situations based on past user-experiences. The system uses what other like-minded users have done in similar context to predict a user's preference towards an item in the current context.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Albrecht Schmidt, Michael Beigl, and Hans-W. Gellersen. There is more to context than location. Computers and Graphics, 23(6):893--901, 1999.
 
2
John S. Breese, David Heckerman, and Carl Kadie. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 43--52, July 1998.
 
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G. Petrone L. Ardissono, A. Goy. INTRIGUE: personalized recommendation of tourist attractions for desktop and handset devices. Applied Artificial Intelligence, 17(8-9):687--714, 2003.
 
4
Mark van Setten, Stanislav Pokraev, and Johan Koolwaaij. Context-Aware Recommendations in the Mobile Tourist Application COMPASS. In Adaptive Hypermedia 2004, volume 3137 ofLNCS, pages 235--244, August 2004.