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KDD
2009
ACM

Mining rich session context to improve web search

10 years 11 months ago
Mining rich session context to improve web search
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...
Guangyu Zhu, Gilad Mishne
Added 25 Nov 2009
Updated 25 Nov 2009
Type Conference
Year 2009
Where KDD
Authors Guangyu Zhu, Gilad Mishne
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