Modeling score distributions for combining the outputs of search engines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Performance prediction using spatial autocorrelation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Estimating retrieval effectiveness using rank distributions
Proceedings of the 17th ACM conference on Information and knowledge management
On score distributions and relevance
ECIR'07 Proceedings of the 29th European conference on IR research
Evaluation of query performance prediction methods by range
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Predicting the performance of recommender systems: an information theoretic approach
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Predicting Query Performance by Query-Drift Estimation
ACM Transactions on Information Systems (TOIS)
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In this paper we introduce a novel approach for query performance prediction based on ranking list scores dispersion. Starting from the hypothesis that different score distributions appear for good and poor performance queries, we introduce a set of measures that capture these differences between both types of distributions. The use of measures based on standard deviation of ranking list scores, as a prediction value, shows a significant correlation degree in terms of average precision.