Randomised Relevance Model
This work addresses efficiency issues in retrieval systems for users needing faster query processing, but it is incremental as it builds on existing Relevance Models with LSH variants.
The paper tackled the slowness of Relevance Models in retrieval by incorporating locality sensitive hashing (LSH) into query expansion, achieving large reductions in computational work with a small reduction in effectiveness, as shown on two document collections.
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.