Paper Info
Title
Boosting Search Performance Using Query Variations
Abstract
Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. Query variations covering the same information need represent one way in which different sources of information might arise. However, when implemented in the obvious manner, fusion over query variations is not cost-effective, at odds with the usual web-search requirement for strict per-query efficiency guarantees. In this work, we propose a novel solution to query fusion by splitting the computation into two parts: one phase that is carried out offline, to generate pre-computed centroid answers for queries addressing broadly similar information needs, and then a second online phase that uses the corresponding topic centroid to compute a result page for each query. To achieve this, we make use of score-based fusion algorithms whose costs can be amortized via the pre-processing step and that can then be efficiently combined during subsequent per-query re-ranking operations. Experimental results using the ClueWeb12B collection and the UQV100 query variations demonstrate that centroid-based approaches allow improved retrieval effectiveness at little or no loss in query throughput or latency and within reasonable pre-processing requirements. We additionally show that queries that do not match any of the pre-computed clusters can be accurately identified and efficiently processed in our proposed ranking pipeline.
Year
DOI
Venue
2019
10.1145/3345001
ACM Transactions on Information Systems
Keywords
Field
DocType
Rank fusion,dynamic pruning,effectiveness,efficiency,experimentation
Information retrieval,Computer science,Boosting (machine learning)
Journal
Volume
Issue
ISSN
37
4
1046-8188
Citations 
PageRank 
References 
2
0.38
0
Authors
4
Name
Order
Citations
PageRank
Rodger Benham173.19
Joel Mackenzie24410.36
Alistair Moffat35913728.91
Shane Culpepper451947.52