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Learning to rank
Results: 420

#	Item
81	Ad hoc, Cross-language and Spoken Document Information Retrieval at IBM Martin Franz, J. Scott McCarley, R. Todd Ward IBM T.J. Watson Research Center P.O. Box 218 Yorktown Heights, NY 10598 Add to Reading List Source URL: trec.nist.gov Language: English Document retrieval Relevance Query expansion Search engine indexing Query language Cross-language information retrieval Relevance feedback Learning to rank Information science Information retrieval Text Retrieval Conference
82	A Learning-to-Rank Approach for Image Color Enhancement Jianzhou Yan1∗ Stephen Lin2 1 Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2014-04-14 03:27:42 Imaging Vision Digital photography Graphic design Image editing Segmentation Color histogram Picasa Feature Image processing Computer vision Computer graphics
83	Helioid at TREC-Legal 2011: Learning to Rank from Relevance Feedback for e-Discovery Peter Lubell-Doughtie and Kenneth Hamilton Helioid Inc., New York, NY, USA {peter,kenneth}@helioid.com Add to Reading List Source URL: trec.nist.gov Language: English - Date: 2012-02-03 09:41:35 Learning to rank Relevance Text Retrieval Conference Query expansion Precision and recall Discounted cumulative gain Document retrieval Google Search Search engine Information science Information retrieval Relevance feedback
84	UCD SIFT in the TREC 2011 Web Track David Leonard, Doychin Doychev, David Lillis, Fergus Toolan, Rem W. Collier, and John Dunnion School of Computer Science and Informatics University College Dublin, Ireland {david.leona Add to Reading List Source URL: trec.nist.gov Language: English - Date: 2012-02-03 09:41:33 Learning to rank Text Retrieval Conference Relevance Document retrieval Precision and recall Discounted cumulative gain Relevance feedback Information science Information retrieval Science
85	Inf Retrieval DOIs10791preprint) The whens and hows of learning to rank for web search Craig Macdonald · Rodrygo L.T. Santos · Add to Reading List Source URL: www.dcs.gla.ac.uk Language: English - Date: 2012-08-21 14:57:59 Learning to rank Precision and recall Relevance IR evaluation Sampling Automatic summarization Ranking function Information science Information retrieval Science
86	Author Model and Negative Feedback Methods on TREC 2011 Microblog Track Rui Li1,2 , Bingjie Wei1,2 , Kai Lu1,2 , Bin Wang1 1 Institute of Computing Technology, Chinese Academy of Sciences Beijing, China, 100190 Add to Reading List Source URL: trec.nist.gov Language: English - Date: 2012-02-03 09:41:30 Relevance feedback Twitter Query expansion Query likelihood model Language model Learning to rank Document classification Stemming Information science Information retrieval Science
87	Predicting the Readability of Short Web Summaries Tapas Kanungo David Orr Add to Reading List Source URL: www.wsdm2009.org Language: English - Date: 2009-04-14 09:42:11 Artificial intelligence Readability Flesch–Kincaid readability test SMOG Automatic summarization Learning to rank Flesch Abstract Human-readable medium Readability tests Science Information science
88	Efficient Multiple-Click Models in Web Search ∗ Fan Guo Carnegie Mellon University Add to Reading List Source URL: www.wsdm2009.org Language: English - Date: 2009-04-14 09:41:44 Point and click Ranking SVM Web search query DOM events Information science Information retrieval Learning to rank
89	Microsoft Word - LR4IR2009.v4-camera-nomark.doc Add to Reading List Source URL: www.thuir.cn Language: English - Date: 2009-06-27 05:08:51 Machine learning Learning Learning to rank Searching Link analysis Ranking SVM PageRank Ranking function Feature selection Information science Information retrieval Statistics

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