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Regularizing query-based retrieval scores (2007)
| Venue: | Information Retrieval |
| Citations: | 15 - 2 self |
BibTeX
@ARTICLE{Diaz07regularizingquery-based,
author = {Fernando Diaz},
title = {Regularizing query-based retrieval scores},
journal = {Information Retrieval},
year = {2007},
volume = {10}
}
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Abstract
Abstract. We adapt the cluster hypothesis for score-based information retrieval by claiming that closely related documents should have similar scores. Given a retrieval from an arbitrary system, we describe an algorithm which directly optimizes this objective by adjusting retrieval scores so that topically related documents receive similar scores. We refer to this process as score regularization. Because score regularization operates on retrieval scores, regardless of their origin, we can apply the technique to arbitrary initial retrieval rankings. Document rankings derived from regularized scores, when compared to rankings derived from un-regularized scores, consistently and significantly result in improved performance given a variety of baseline retrieval algorithms. We also present several proofs demonstrating that regularization generalizes methods such as pseudo-relevance feedback, document expansion, and cluster-based retrieval. Because of these strong empirical and theoretical results, we argue for the adoption of score regularization as general design principle or post-processing step for information retrieval systems.
Keyphrases
query-based retrieval score score regularization similar score retrieval score related document general design principle cluster hypothesis arbitrary initial retrieval ranking score-based information retrieval post-processing step strong empirical arbitrary system document expansion theoretical result improved performance baseline retrieval algorithm information retrieval system pseudo-relevance feedback several proof un-regularized score cluster-based retrieval regularization generalizes method document ranking regularized score
