Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Characterizing browsing strategies in the World-Wide Web
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
Learning user's preferences by analyzing Web-browsing behaviors
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Personal ontologies for web navigation
Proceedings of the ninth international conference on Information and knowledge management
Proceedings of the 6th international conference on Intelligent user interfaces
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback
Distributed and Parallel Databases
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Personalized Search Based on User Search Histories
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Journal of the American Society for Information Science and Technology
Off the beaten tracks: exploring three aspects of web navigation
Proceedings of the 15th international conference on World Wide Web
Behavior-Based Web Page Evaluation
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Context Oriented Analysis of Interest Reflection of Tweeted Webpages based on Browsing Behavior
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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This paper describes our efforts to investigate factors in user browsing behavior to automatically evaluate Web pages that the user shows interest in. We developed a client site logging tool to monitor and log the user's browsing behavior. We performed user experiment using ten participants to collect the browsing behavior, and evaluated the behaviors by performing classification learning using C4.5. We generated common user browsing behavior rules and evaluated these common rules against the individual participant data. This paper reports those findings.