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» Learning to rank for information retrieval (LR4IR 2008)
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SIGIR
2008
ACM
14 years 11 months ago
Novelty and diversity in information retrieval evaluation
Evaluation measures act as objective functions to be optimized by information retrieval systems. Such objective functions must accurately reflect user requirements, particularly w...
Charles L. A. Clarke, Maheedhar Kolla, Gordon V. C...
WSDM
2012
ACM
267views Data Mining» more  WSDM 2012»
13 years 7 months ago
Learning to rank with multi-aspect relevance for vertical search
Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-d...
Changsung Kang, Xuanhui Wang, Yi Chang, Belle L. T...
SIGIR
2009
ACM
15 years 6 months ago
Combining LVCSR and vocabulary-independent ranked utterance retrieval for robust speech search
Well tuned Large-Vocabulary Continuous Speech Recognition (LVCSR) has been shown to generally be more effective than vocabulary-independent techniques for ranked retrieval of spo...
J. Scott Olsson, Douglas W. Oard
ERCIMDL
2009
Springer
117views Education» more  ERCIMDL 2009»
15 years 6 months ago
A Visualization Tool of Probabilistic Models for Information Access Components
An effective graphic interface is a key tool to improve the fruition of the results retrieved by an Information Retrieval (IR) system. In this work, we describe a two-dimensional...
Lorenzo De Stefani, Giorgio Maria Di Nunzio, Giorg...
KDD
2007
ACM
192views Data Mining» more  KDD 2007»
16 years 3 days ago
Active exploration for learning rankings from clickthrough data
We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. ...
Filip Radlinski, Thorsten Joachims