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The nature of statistical learning theory
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Inductive learning algorithms and representations for text categorization
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UM '99 Proceedings of the seventh international conference on User modeling
Mining navigation history for recommendation
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Agglomerative clustering of a search engine query log
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Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
User Modeling and User-Adapted Interaction
GOOSE: A Goal-Oriented Search Engine with Commonsense
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Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Letizia: an agent that assists web browsing
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Adaptive web navigation for wireless devices
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Performance Evaluation of Mobile Agents for Knowledge-Based Web Information Services
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Personalised Information Retrieval: survey and classification
User Modeling and User-Adapted Interaction
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World Wide Web search engines typically return thousands of results to the users. To avoid users browsing through the whole list of results, search engines use ranking algorithms to order the list according to predefined criteria. In this paper, we present Toogle, a front-end to the Google search engine for both desktop browsers and mobile phones. For a given search query, Toogle first ranks results using Google's algorithm and, as the user browses through the result list, uses machine learning techniques to infer a model of her search goal and to adapt accordingly the order in which the results are presented. We describe preliminary experimental results that show the effectiveness of Toogle.