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Adaptive Web sites: automatically synthesizing Web pages
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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Philip and Alex's guide to Web publishing
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UM '99 Proceedings of the seventh international conference on User modeling
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Automatic personalization based on Web usage mining
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Artificial Intelligence - Special issue on Intelligent internet systems
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Knowledge and Information Systems
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Data Mining and Knowledge Discovery
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IEEE Transactions on Knowledge and Data Engineering
Clustering the Users of Large Web Sites into Communities
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Integrating E-Commerce and Data Mining: Architecture and Challenges
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Challenges of Delivering Content on the Internet
WADS '01 Proceedings of the 7th International Workshop on Algorithms and Data Structures
Feature Matrices: A Model for Efficient and Anonymous Web Usage Mining
EC-Web 2001 Proceedings of the Second International Conference on Electronic Commerce and Web Technologies
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WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Yoda: An Accurate and Scalable Web-Based Recommendation System
CooplS '01 Proceedings of the 9th International Conference on Cooperative Information Systems
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WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
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RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
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TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
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ACM SIGKDD Explorations Newsletter
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UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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IEEE Intelligent Systems
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Data & Knowledge Engineering
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COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
Preference-Function Algorithm: a novel approach for selection of the users' preferred websites
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International Journal of Computer Applications in Technology
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WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
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Web Usage Mining (WUM), a natural application of data mining techniques to the data collected from user interactions with the web, has greatly concerned both academia and industry in recent years. Through WUM, we are able to gain a better understanding of both the web and web user access patterns; a knowledge that is crucial for realization of full economic potential of the web. In this chapter, we describe a framework for WUM that particularly satisfies the challenging requirements of the web personalization applications. For on-line and anonymous web personalization to be effective, WUM must be accomplished in real-time as accurately as possible. On the other hand, the analysis tier of the WUM system should allow compromise between scalability and accuracy to be applicable to real-life web-sites with numerous visitors. Within our WUM framework, we introduce a distributed user tracking approach for accurate, efficient, and scalable collection of the usage data. We also propose a new model, the Feature Matrices (FM) model, to capture and analyze users access patterns. With FM, various features of the usage data can be captured with flexible precision so that we can trade off accuracy for scalability based on the specific application requirements. Moreover, due to low update complexity of the model, FM can adapt to user behavior changes in real-time. Finally, we define a novel similarity measure based on FM that is specifically designed for accurate classification of partial navigation patterns in real-time. Our extensive experiments with both synthetic and real data verify correctness and efficacy of our WUM framework for efficient web personalization.