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» Using RankBoost to compare retrieval systems
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CIKM
2005
Springer
13 years 5 months ago
Using RankBoost to compare retrieval systems
This paper presents a new pooling method for constructing the assessment sets used in the evaluation of retrieval systems. Our proposal is based on RankBoost, a machine learning v...
Huyen-Trang Vu, Patrick Gallinari
SIGIR
2009
ACM
13 years 10 months ago
Combining audio content and social context for semantic music discovery
When attempting to annotate music, it is important to consider both acoustic content and social context. This paper explores techniques for collecting and combining multiple sourc...
Douglas Turnbull, Luke Barrington, Gert R. G. Lanc...
IR
2010
13 years 2 months ago
Adapting boosting for information retrieval measures
Abstract We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empiricall...
Qiang Wu, Christopher J. C. Burges, Krysta Marie S...
GECCO
2009
Springer
151views Optimization» more  GECCO 2009»
13 years 10 months ago
Swarming to rank for information retrieval
This paper presents an approach to automatically optimize the retrieval quality of ranking functions. Taking a Swarm Intelligence perspective, we present a novel method, SwarmRank...
Ernesto Diaz-Aviles, Wolfgang Nejdl, Lars Schmidt-...
CIKM
2009
Springer
13 years 10 months ago
Learning to rank from Bayesian decision inference
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang