The nature of statistical learning theory
The nature of statistical learning theory
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Evaluating relevance feedback algorithms for searching on small displays
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Automated user modeling for personalized digital libraries
International Journal of Information Management: The Journal for Information Professionals
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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 mobile phones offering web browsing. For a given search query, Toogle first ranks results using Googles 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 yet-unseen results are presented. We report preliminary experimental results that show the effectiveness of this approach