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SIGIR
2011
ACM
14 years 9 months ago
Learning to rank from a noisy crowd
We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Abhimanu Kumar, Matthew Lease
SPIRE
2005
Springer
15 years 11 months ago
Retrieval Status Values in Information Retrieval Evaluation
Retrieval systems rank documents according to their retrieval status values (RSV) if these are monotonously increasing with the probability of relevance of documents. In this work,...
Amélie Imafouo, Xavier Tannier
ICDE
2008
IEEE
425views Database» more  ICDE 2008»
17 years 5 months ago
NAGA: Searching and Ranking Knowledge
The Web has the potential to become the world’s largest knowledge base. In order to unleash this potential, the wealth of information available on the Web needs to be extracte...
Gjergji Kasneci, Fabian M. Suchanek, Georgiana Ifr...
CIKM
2009
Springer
16 years 22 days ago
Learning to rank graphs for online similar graph search
Many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. Prior works, especially in chem...
Bingjun Sun, Prasenjit Mitra, C. Lee Giles
ICDIM
2007
IEEE
16 years 14 days ago
Several methods of ranking retrieval systems with partial relevance judgment
: Some measures such as average precision over all relevant documents and recall level precision are considered as good system-oriented measures, because they concern both precisio...
Shengli Wu, Sally I. McClean