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» Learning to rank for information retrieval
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MM
2004
ACM
152views Multimedia» more  MM 2004»
14 years 1 months ago
Manifold-ranking based image retrieval
In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ra...
Jingrui He, Mingjing Li, HongJiang Zhang, Hanghang...
SIGIR
2008
ACM
13 years 7 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...
ICTIR
2009
Springer
14 years 2 months ago
The Quantum Probability Ranking Principle for Information Retrieval
Abstract. While the Probability Ranking Principle for Information Retrieval provides the basis for formal models, it makes a very strong assumption regarding the dependence between...
Guido Zuccon, Leif Azzopardi, Keith van Rijsbergen
ECML
2007
Springer
13 years 9 months ago
Sequence Labeling with Reinforcement Learning and Ranking Algorithms
Many problems in areas such as Natural Language Processing, Information Retrieval, or Bioinformatic involve the generic task of sequence labeling. In many cases, the aim is to assi...
Francis Maes, Ludovic Denoyer, Patrick Gallinari
AIRS
2010
Springer
13 years 5 months ago
Relevance Ranking Using Kernels
This paper is concerned with relevance ranking in search, particularly that using term dependency information. It proposes a novel and unified approach to relevance ranking using ...
Jun Xu, Hang Li, Chaoliang Zhong