Randomised Relevance Model

TL;DR

This paper introduces a method to speed up relevance models in information retrieval by integrating locality sensitive hashing, significantly reducing computational effort with minimal impact on effectiveness.

Contribution

It proposes incorporating LSH variants into relevance models to enhance query expansion efficiency, a novel approach to address their slowness.

Findings

Large reductions in computational work achieved

Small decrease in retrieval effectiveness observed

Method is additive when pruning query terms

Abstract

Relevance Models are well-known retrieval models and capable of producing competitive results. However, because they use query expansion they can be very slow. We address this slowness by incorporating two variants of locality sensitive hashing (LSH) into the query expansion process. Results on two document collections suggest that we can obtain large reductions in the amount of work, with a small reduction in effectiveness. Our approach is shown to be additive when pruning query terms.