摘要: The techniques discussed herein consider a degree of noise associated with user clicks performed during search sessions. then generate model that characterizes click so engines can more accurately infer document relevance.
lens.org 参考文章(28)
Benyu Zhang, Wei-Ying Ma, Hua-Jun Zeng, Gui-Rong Xue, Zheng Chen, Determining relevance using queries as surrogate content ,(2005)
Milad Shokouhi, Michael J. Taylor, Filiip Radlinski, Merging search results ,(2010)
Robert J. Ragno, Eric D. Brill, Yevgeny E. Agichtein, Susan T. Dumais, Mining web search user behavior to enhance web search relevance ,(2007)
Olivier Chapelle, Shihao Ji, Zhaohui Zheng, Anlei Dong, Ciya Liao, Gordon Guo-Zheng Sun, Yi Chang, Hongyuan Zha, Global and topical ranking of search results using user clicks ,(2009)
Vanessa Murdock, Vassilis Plachouras, Massimiliano Ciaramita, Optimizing ranking functions using click data ,(2008)
Martin Szummer, Nicholas Craswell, Information retrieval using query-document pair information ,(2007)
Olivier Chapelle, Anne Ya Zhang, Click model for search rankings ,(2008)
Rosie Jones, Ben Carterette, System and method for predicting clickthrough rates and relevance ,(2007)
Zhaohui Zheng, Gordon Sun, System and method for indexing web content using click-through features ,(2006)
Iftah Gamzu, Joseph Yossi Azar, Xiaoxin Yin, Ranking results of multiple intent queries ,(2009)