Adapting to the user's internet search strategy on small devices

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