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ICML
1998
IEEE
14 years 6 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
CIKM
2010
Springer
13 years 4 months ago
Online learning for recency search ranking using real-time user feedback
Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learni...
Taesup Moon, Lihong Li, Wei Chu, Ciya Liao, Zhaohu...
WSDM
2012
ACM
267views Data Mining» more  WSDM 2012»
12 years 1 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
2010
ACM
13 years 9 months ago
Learning more powerful test statistics for click-based retrieval evaluation
Interleaving experiments are an attractive methodology for evaluating retrieval functions through implicit feedback. Designed as a blind and unbiased test for eliciting a preferen...
Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, T...
GISCIENCE
2004
Springer
159views GIS» more  GISCIENCE 2004»
13 years 11 months ago
The SPIRIT Spatial Search Engine: Architecture, Ontologies and Spatial Indexing
Abstract. The SPIRIT search engine provides a test bed for the development of web search technology that is specialised for access to geographical information. Major components inc...
Christopher B. Jones, Alia I. Abdelmoty, David Fin...