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
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic query wefinement using lexical affinities with maximal information gain
SIGIR '02 Proceedings of the 25th 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
Data fusion with estimated weights
Proceedings of the eleventh international conference on Information and knowledge management
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A framework for selective query expansion
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Proceedings of the 28th 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
Ranking robustness: a novel framework to predict query performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Combining fields for query expansion and adaptive query expansion
Information Processing and Management: an International Journal
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
Performance prediction using spatial autocorrelation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Towards robust query expansion: model selection in the language modeling framework
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
When is query performance prediction effective?
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
Recent developments in information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Automatic refinement of patent queries using concept importance predictors
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Document Score Distribution Models for Query Performance Inference and Prediction
ACM Transactions on Information Systems (TOIS)
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Query performance predictors are commonly evaluated by reporting correlation coefficients to denote how well the methods perform at predicting the retrieval performance of a set of queries. Despite the amount of research dedicated to this area, one aspect remains neglected: how strong does the correlation need to be in order to realize an improvement in retrieval effectiveness in an operational setting? We address this issue in the context of two settings: Selective Query Expansion and Meta-Search. In an empirical study, we control the quality of a predictor in order to examine how the strength of the correlation achieved, affects the effectiveness of an adaptive retrieval system. The results of this study show that many existing predictors fail to achieve a correlation strong enough to reliably improve the retrieval effectiveness in the Selective Query Expansion as well as the Meta-Search setting.