From user access patterns to dynamic hypertext linking
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Ariadne: a Java-based guided tour system for the World Wide Web
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the third annual conference on Autonomous Agents
Footprints: history-rich tools for information foraging
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Mining navigation history for recommendation
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Visualization of navigation patterns on a Web site using model-based clustering
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Personalizing web sites for mobile users
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Adaptive web sites: cluster mining and conceptual clustering for index page synthesis
Adaptive web sites: cluster mining and conceptual clustering for index page synthesis
Adaptive web sites: an AI challenge
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Trading off granularity against complexity in predictive models for complex domains
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Personalized portals for the wireless user based on mobile agents
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AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
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IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
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UM'03 Proceedings of the 9th international conference on User modeling
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MARS: an agent-based recommender system for the semantic web
DAIS'07 Proceedings of the 7th IFIP WG 6.1 international conference on Distributed applications and interoperable systems
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Visitors who browse the web from wireless PDAs, cell phones, and pagers are frequently stymied by web interfaces optimized for desktop PCs. Simply replacing graphics with text and reformatting tables does not solve the problem, because deep link structures can still require minutes to traverse. In this paper we develop an algorithm, MINPATH, that automatically improves wireless web navigation by suggesting useful shortcut links in real time. MINPATH finds shortcuts by using a learned model of web visitor behavior to estimate the savings of shortcut links, and suggests only the few best links. We explore a variety of predictive models, including Naïve Bayes mixture models and mixtures of Markov models, and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort.