Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
On ranking the effectiveness of searches
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Query performance prediction in web search environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query performance prediction: evaluation contrasted with effectiveness
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Document Score Distribution Models for Query Performance Inference and Prediction
ACM Transactions on Information Systems (TOIS)
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The utility of Query Performance Prediction (QPP) methods is commonly evaluated by reporting correlation coefficients to denote how well the methods perform at predicting the retrieval performance of a set of queries. However, a quintessential question remains unexplored: how strong does the correlation need to be in order to realize an increase in retrieval performance? In this work, we address this question in the context of Selective Query Expansion (SQE) and perform a large-scale experiment. The results show that to consistently and predictably improve retrieval effectiveness in the ideal SQE setting, a Kendall's Tau correlation of tau=0.5 is required, a threshold which most existing query performance prediction methods fail to reach.