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CIKM
2010
Springer

A unified optimization framework for robust pseudo-relevance feedback algorithms

8 years 10 months ago
A unified optimization framework for robust pseudo-relevance feedback algorithms
We present a flexible new optimization framework for finding effective, reliable pseudo-relevance feedback models that unifies existing complementary approaches in a principled way. The result is an algorithmic approach that not only brings together different benefits of previous methods, such as parameter self-tuning and risk reduction from term dependency modeling, but also allows a rich new space of model search strategies to be investigated. We compare the effectiveness of a unified algorithm to existing methods by examining iterative performance and risk-reward tradeoffs. We also discuss extensions for generating new algorithms within our framework. Categories and Subject Descriptors: H.3.3 [Information Retrieval]: Retrieval Models General Terms: Algorithms, Experimentation
Joshua V. Dillon, Kevyn Collins-Thompson
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where CIKM
Authors Joshua V. Dillon, Kevyn Collins-Thompson
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