JOURNAL ARTICLE

Query-drift prevention for robust query expansion

Abstract

Pseudo-feedback-based automatic query expansion yields effective retrieval performance on average, but results in performance inferior to that of using the original query for many information needs. We address an important cause of this robustness issue, namely, the query drift problem, by fusing the results retrieved in response to the original query and to its expanded form. Our approach posts performance that is significantly better than that of retrieval based only on the original query and more robust than that of retrieval using the expanded query.

Keywords:
Query expansion Query optimization Sargable Computer science Web query classification Robustness (evolution) Web search query Query language Information retrieval Online aggregation Relevance feedback RDF query language Query by Example Data mining Search engine Image retrieval Artificial intelligence Chemistry

Metrics

44
Cited By
7.15
FWCI (Field Weighted Citation Impact)
18
Refs
0.97
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Information Retrieval and Search Behavior
Physical Sciences →  Computer Science →  Information Systems
Mobile Crowdsensing and Crowdsourcing
Physical Sciences →  Computer Science →  Computer Science Applications
Algorithms and Data Compression
Physical Sciences →  Computer Science →  Artificial Intelligence

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