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Can we predict QPP? An approach based on multivariate outliers
Query performance prediction (QPP) aims to forecast the effectiveness of a search engine across a range of queries and documents.
Selective Query Processing: a Risk-Sensitive Selection of System Configurations
To determine the ideal configurations to use on a per-query basis in real-world systems we developed a method in which a restricted number of possible configurations is pre-selected and then used in a meta-search engine that decides the best search configuration on a per query basis.
Effectiveness and Efficiency Trade-off in Selective Query Processing
In this paper, we examine selective query processing in different settings, both in terms of effectiveness and efficiency; this includes selective query expansion and other forms of selective query processing (e. g., when the term weighting function varies or when the expansion model varies).
Forward and Backward Feature Selection for Query Performance Prediction
We found that: (1) our model based on a limited number of selected features is as good as more complex models for QPP and better than non-selective models; (2) our model is more efficient than complex models during inference time since it requires fewer features; (3) the predictive model is readable and understandable; and (4) one of our new QPP features is consistently selected across different collections, proving its usefulness.
