Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PHOAKS: a system for sharing recommendations
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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In a ubiquitous exhibition, an intelligent navigation service that can provide booths' information, recommend interesting booths and plan touring path is required for both visitors and vendors. The preference mining module is the kernel. This paper proposes a group-based user preference pattern mining method, which can be implemented as a preference mining module in this service. When the visiting traces that imply the preference of users are recorded, the method discovers user preference patterns with high representativeness and high discrimination from the historical visiting logs. According to the discovered model, collaborative recommendation can be accomplished, and then the intelligent navigation service can plan personalized touring path based on the recommendation lists. For demonstrating the performance of the proposed method, we engage some experiments, and then indicate the characteristics of the proposed method.