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IR evaluation methods for retrieving highly relevant documents
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WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
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HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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User browsing information, particularly their non-search related activity, reveals important contextual information on the preferences and the intent of web users. In this paper, we expand the use of browsing information for web search ranking and other applications, with an emphasis on analyzing individual user sessions for creating aggregate models. In this context, we introduce ClickRank, an efficient, scalable algorithm for estimating web page and web site importance from browsing information. We lay out the theoretical foundation of ClickRank based on an intentional surfer model and analyze its properties. We evaluate its effectiveness for the problem of web search ranking, showing that it contributes significantly to retrieval performance as a novel web search feature. We demonstrate that the results produced by ClickRank for web search ranking are highly competitive with those produced by other approaches, yet achieved at better scalability and substantially lower computational costs. Finally, we discuss novel applications of ClickRank in providing enriched user web search experience, highlighting the usefulness of our approach for non-ranking tasks.