Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating document clustering for interactive information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Information Retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The effectiveness of query-specific hierarchic clustering in information retrieval
Information Processing and Management: an International Journal
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Corpus structure, language models, and ad hoc information retrieval
Proceedings of the 27th 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
Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models
SIGIR '06 Proceedings of the 29th 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
On ranking the effectiveness of searches
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Representing clusters for retrieval
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
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
The opposite of smoothing: a language model approach to ranking query-specific document clusters
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A rank-aggregation approach to searching for optimal query-specific clusters
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
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
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
Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions
ECIR'07 Proceedings of the 29th European conference on IR research
Evaluating text representations for retrieval of the best group of documents
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Geometric representations for multiple documents
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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
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
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)
Predicting query performance directly from score distributions
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
The optimum clustering framework: implementing the cluster hypothesis
Information Retrieval
Ranking document clusters using markov random fields
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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We show that two tasks which were independently addressed in the information retrieval literature actually amount to the exact same task. The first is query performance prediction; i.e., estimating the effectiveness of a search performed in response to a query in the absence of relevance judgments. The second task is cluster ranking, that is, ranking clusters of similar documents by their presumed effectiveness (i.e., relevance) with respect to the query. Furthermore, we show that several state-of-the-art methods that were independently devised for each of the two tasks are based on the same principles. Finally, we empirically demonstrate that using insights gained in work on query-performance prediction can help, in many cases, to improve the performance of a previously proposed cluster ranking method.