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Mining rich session context to improve web search
2009
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
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
doi:10.1145/1557019.1557131
dblp:conf/kdd/ZhuM09
fatcat:6se74mfubfb3lix6unbrnlg2ba