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» Top-k learning to rank: labeling, ranking and evaluation
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ISDA
2009
IEEE
15 years 4 months ago
Evaluation Measures for Ordinal Regression
—Ordinal regression (OR – also known as ordinal classification) has received increasing attention in recent times, due to its importance in IR applications such as learning to...
Stefano Baccianella, Andrea Esuli, Fabrizio Sebast...
73
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SIGIR
2008
ACM
14 years 9 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...
HT
2006
ACM
15 years 3 months ago
Implementation and evaluation of a quality-based search engine
In this paper, an approach for the implementation of a qualitybased Web search engine is proposed. Quality retrieval is introduced and an overview on previous efforts to implement...
Thomas Mandl
WSDM
2009
ACM
140views Data Mining» more  WSDM 2009»
15 years 4 months ago
Effective latent space graph-based re-ranking model with global consistency
Recently the re-ranking algorithms have been quite popular for web search and data mining. However, one of the issues is that those algorithms treat the content and link informati...
Hongbo Deng, Michael R. Lyu, Irwin King
CIKM
2008
Springer
14 years 11 months ago
Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
We introduce a multi-stage ensemble framework, ErrorDriven Generalist+Expert or Edge, for improved classification on large-scale text categorization problems. Edge first trains a ...
Jian Huang 0002, Omid Madani, C. Lee Giles