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» Search Engines that Learn from Implicit Feedback
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INFOCOM
2010
IEEE
14 years 9 months ago
Surfing the Blogosphere: Optimal Personalized Strategies for Searching the Web
We propose a distributed mechanism for finding websurfing strategies that is inspired by the StumbleUpon recommendation engine. Each day, a websurfer visits a sequence of websites ...
Stratis Ioannidis, Laurent Massoulié
EDBTW
2004
Springer
15 years 4 months ago
Relevance Feedback in XML Retrieval
Highly heterogeneous XML data collections that do not have a global schema, as arising, for example, in federations of digital libraries or scientific data repositories, cannot be...
Hanglin Pan
CIKM
2008
Springer
15 years 1 months ago
Towards a model of understanding social search
Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon...
Brynn M. Evans, Ed H. Chi
WWW
2008
ACM
15 years 11 months ago
Learning to rank relational objects and its application to web search
Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becom...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang...
WEBI
2010
Springer
14 years 9 months ago
Incorporating Seasonality into Search Suggestions Derived from Intranet Query Logs
While much research has been performed on query logs collected for major Web search engines, query log analysis to enhance search on smaller and more focused collections has attrac...
Stephen Dignum, Udo Kruschwitz, Maria Fasli, Yunhy...