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Formal sciences / Information retrieval / Learning to rank / Regression analysis / Data mining / Error detection and correction / Errors and residuals in statistics / Support vector machine / Ranking function / Statistics / Machine learning / Measurement


Improving Quality of Training Data for Learning to Rank Using Click-Through Data Jingfang Xu Chuanliang Chen
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Document Date: 2009-12-30 01:29:22


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File Size: 682,86 KB

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City

Beijing / New York City / /

Company

Neural Information Processing Systems / Amazon / Morgan Kaufmann Publishers Inc. / Vertex / Microsoft / /

Country

United States / /

Currency

USD / /

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Event

Product Issues / Product Recall / /

Facility

Computer Science Beijing Normal University / /

IndustryTerm

search engines using clickthrough data / query processing / data mining approach / data mining methods / evaluation tools / search log / efficient boosting algorithm / list-wise algorithms / data mining / personalized search / pair-wise algorithms / online marketplace / search system / web data set / search engines / large scale web search data / Web Search Click Data / web search / web search data / search engine / learning algorithms / retrieval systems / /

Organization

P.R.China Department of Computer Science Beijing Normal University Beijing / Q. Mei / D. Zhou / and K. Church / /

Person

Chen Gu Xu / Pr / Renato Torres Abib / Redmond / /

Position

professional judge / model the relationship / local model / model / ABSTRACT General / eld model for term dependencies / judge / /

Product

error detection method / HITs / prediction / 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 / /

ProgrammingLanguage

D / /

ProvinceOrState

New York / /

PublishedMedium

Machine Learning / /

Technology

Viterbi algorithm / L-BFGS algorithm / search engine / machine learning / html / list-wise algorithms / knowledge management / boosting algorithm / efficient boosting algorithm / learning algorithms / data mining / two list-wise algorithms / simulation / FDM / pair-wise algorithms / /

URL

www.mturk.com/mturk/welcome / http /

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