#6327. Pseudo relevance feedback optimization

September 2026publication date
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Journal’s subject area:
Library and Information Sciences;
Information Systems;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
We propose a method for automatic optimization of pseudo relevance feedback (PRF) in information retrieval. Based on the conjecture that the initial query’s contribution to the final query may not be necessary once a good model is built from pseudo relevant documents, we set out to optimize per query only the number of top-retrieved documents to be used for feedback. The optimization is based on several query performance predictors for the initial query, by building a linear regression model discovering the optimal machine learning pipeline via genetic programming.
Keywords:
Blind relevance feedback; Optimization; Pseudo relevance feedback; Query difficulty; Query performance predictors; Regression

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