A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
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
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Precision prediction based on ranked list coherence
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
A new rank correlation coefficient for information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Improved query difficulty prediction for the web
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
Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions
ECIR'07 Proceedings of the 29th European conference on IR research
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
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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 premise 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 proposed measures will employ the ranking list, output of a search system, as an information source to predict query performance in terms of MAP. The obtained results reveal a significant correlation degree with MAP and are very similar to those achievedwithmore complexmethods. Finally some generic open questions that could guide further research on query prediction methods are introduced.