We introduce a new theoretical derivation, evaluation methods,

and extensive empirical analysis for an automatic query

expansion framework in which model estimation is cast as a

robust constrained optimization problem. This framework

provides a powerful method for modeling and solving complex

expansion problems, by allowing multiple sources of

domain knowledge or evidence to be encoded as simultaneous

optimization constraints. Our robust optimization approach

provides a clean theoretical way to model not only

expansion benefit, but also expansion risk, by optimizing

over uncertainty sets for the data. In addition, we introduce

risk-reward curves to visualize expansion algorithm performance

and analyze parameter sensitivity. We show that a

robust approach significantly reduces the number and magnitude

of expansion failures for a strong baseline algorithm,

with no loss in average gain. Our approach is implemented

as a highly efficient post-processing step that assumes little

about the baseline expansion method used as input, making

it easy to apply to existing expansion methods. We provide

analysis showing that this approach is a natural and effective

way to do selective expansion, automatically reducing

or avoiding expansion in risky scenarios, and successfully

attenuating noise in poor baseline methods.

In  Proceedings of CIKM 2009

Publisher  Association for Computing Machinery, Inc.
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