Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Creating Adaptive Web Sites Through Usage-Based Clustering of URLs
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Recommendation models for user accesses to web pages
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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For many web usage mining applications, it is crucial to compare navigation paths of different users. This paper presents a reinforcement learning based method for mining the sequential usage patterns of user behaviors. In detail, the temporal data set about every user is constructed from the web log file, and then the navigation paths of the users are modelled using the extended Markov decision process. The proposed method could learn the dynamical sequential usage patterns on-line.