Algorithms for clustering data
Algorithms for clustering data
Information Retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query-sensitive similarity measures for information retrieval
Knowledge and Information Systems
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
Enhancing digital libraries using missing content analysis
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Measuring ranked list robustness for query performance prediction
Knowledge and Information Systems
Improved query difficulty prediction for the web
Proceedings of the 17th ACM conference on Information and knowledge management
The Combination and Evaluation of Query Performance Prediction Methods
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Query performance prediction for information retrieval based on covering topic score
Journal of Computer Science and Technology
Reducing long queries using query quality predictors
Proceedings of the 32nd 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
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
Measuring system performance and topic discernment using generalized adaptive-weight mean
Proceedings of the 18th ACM conference on Information and knowledge management
Using statistical decision theory and relevance models for query-performance prediction
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A comparison of user and system query performance predictions
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
LambdaMerge: merging the results of query reformulations
Proceedings of the fourth ACM international conference on Web search and data mining
A unified framework for post-retrieval query-performance prediction
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Predicting query performance via classification
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Query performance prediction: evaluation contrasted with effectiveness
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Predicting Query Performance by Query-Drift Estimation
ACM Transactions on Information Systems (TOIS)
On per-topic variance in IR evaluation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Query performance prediction for IR
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Keyphrase extraction through query performance prediction
Journal of Information Science
Predicting query performance for fusion-based retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
Back to the roots: a probabilistic framework for query-performance prediction
Proceedings of the 21st ACM international conference on Information and knowledge management
Query-performance prediction and cluster ranking: two sides of the same coin
Proceedings of the 21st ACM international conference on Information and knowledge management
Using document-quality measures to predict web-search effectiveness
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Increasing evaluation sensitivity to diversity
Information Retrieval
Combining pre-retrieval query quality predictors using genetic programming
Applied Intelligence
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There is a growing interest in estimating the effectiveness of search. Two approaches are typically considered: examining the search queries and examining the retrieved document sets. In this paper, we take the latter approach. We use four measures to characterize the retrieved document sets and estimate the quality of search. These measures are (i) the clustering tendency as measured by the Cox-Lewis statistic, (ii) the sensitivity to document perturbation, (iii) the sensitivity to query perturbation and (iv) the local intrinsic dimensionality. We present experimental results for the task of ranking 200 queries according to the search effectiveness over the TREC (discs 4 and 5) dataset. Our ranking of queries is compared with the ranking based on the average precision using the Kendall t statistic. The best individual estimator is the sensitivity to document perturbation and yields Kendall t of 0.521. When combined with the clustering tendency based on the Cox-Lewis statistic and the query perturbation measure, it results in Kendall t of 0.562 which to our knowledge is the highest correlation with the average precision reported to date.