%0 Conference Paper %1 Zhu09 %A Zhu, Guangyu %A Mishne, Gilad %B KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining %C New York, NY, USA %D 2009 %I ACM %K SocialMedia WebSearch %P 1037--1046 %R http://doi.acm.org/10.1145/1557019.1557131 %T Mining rich session context to improve web search %U http://portal.acm.org/citation.cfm?id=1557019.1557131 %X 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. %@ 978-1-60558-495-9

@inproceedings{Zhu09, abstract = {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.}, added-at = {2009-11-02T15:23:35.000+0100}, address = {New York, NY, USA}, author = {Zhu, Guangyu and Mishne, Gilad}, biburl = {https://www.bibsonomy.org/bibtex/26af39b4a1af00d38a308532ea2959b19/mkroell}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, description = {Mining rich session context to improve web search}, doi = {http://doi.acm.org/10.1145/1557019.1557131}, interhash = {57e0dc6edf7189603ede20ca71c493f1}, intrahash = {6af39b4a1af00d38a308532ea2959b19}, isbn = {978-1-60558-495-9}, keywords = {SocialMedia WebSearch}, location = {Paris, France}, pages = {1037--1046}, publisher = {ACM}, timestamp = {2009-11-02T15:23:35.000+0100}, title = {Mining rich session context to improve web search}, url = {http://portal.acm.org/citation.cfm?id=1557019.1557131}, year = 2009 }