Communications of the ACM - Special issue on information filtering
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Peer-to-peer based recommendations for mobile commerce
WMC '01 Proceedings of the 1st international workshop on Mobile commerce
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Learning user similarity and rating style for collaborative recommendation
ECIR'03 Proceedings of the 25th European conference on IR research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Utilizing Popularity Characteristics for Product Recommendation
International Journal of Electronic Commerce
A Novel Method Providing Multimedia Contents According to Preference Clones in Mobile Environment
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Delay-tolerant collaborative filtering
Proceedings of the 7th ACM international symposium on Mobility management and wireless access
Rush: repeated recommendations on mobile devices
Proceedings of the 15th international conference on Intelligent user interfaces
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This paper describes how collaborative filtering can be used for mobile devices. When the user is connected to a central repository, the algorithm selects a subset of profiles to store on the device. When the user is not connected to the repository, the predictions can be incrementally updated to reflect new or updated ratings. Experiments on a movie data set show that the method can dramatically reduce the data needed while still performing nearly as good as a centralized approach.