SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A survey of pre-retrieval query performance predictors
Proceedings of the 17th ACM conference on Information and knowledge management
Reducing long queries using query quality predictors
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Predicting Query Performance by Query-Drift Estimation
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Effective pre-retrieval query performance prediction using similarity and variability evidence
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Exploring reductions for long web queries
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Estimating the Query Difficulty for Information Retrieval
Estimating the Query Difficulty for Information Retrieval
Standard deviation as a query hardness estimator
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
The limits of retrieval effectiveness
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Improved query performance prediction using standard deviation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Predicting Query Performance by Query-Drift Estimation
ACM Transactions on Information Systems (TOIS)
Modeling reformulation using query distributions
ACM Transactions on Information Systems (TOIS)
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Query performance prediction (QPP) aims to automatically estimate the performance of a query. Recently there have been many attempts to use these predictors to estimate whether a perturbed version of a query will outperform the original version. In essence, these approaches attempt to navigate the space of queries in a guided manner. In this paper, we perform an analysis of the query space over a substantial number of queries and show that (1) users tend to be able to extract queries that perform in the top 5% of all possible user queries for a specific topic, (2) that post-retrieval predictors outperform preretrieval predictors at the high end of the query space. And, finally (3), we show that some post retrieval predictors are better able to select high performing queries from a group of user queries for the same topic.