Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A general language model for information retrieval (poster abstract)
Proceedings of the 22nd 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
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
A Markov random field model for term dependencies
Proceedings of the 28th 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
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
Precision prediction based on ranked list coherence
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
A survey of pre-retrieval query performance predictors
Proceedings of the 17th ACM conference on Information and knowledge management
Retrieval performance prediction and document quality
Retrieval performance prediction and document quality
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
A comparison of user and system query performance predictions
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Improved query performance prediction using standard deviation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A unified framework for post-retrieval query-performance prediction
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Predicting the performance of recommender systems: an information theoretic approach
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Navigating the user query space
SPIRE'11 Proceedings of the 18th international conference on String processing and 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
Investigating performance predictors using monte carlo simulation and score distribution models
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
Back to the roots: a probabilistic framework for query-performance prediction
Proceedings of the 21st ACM international conference on Information and knowledge management
Predicting the performance of passage retrieval for question answering
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
Estimating query representativeness for query-performance prediction
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A Standard Document Score for Information Retrieval
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Increasing evaluation sensitivity to diversity
Information Retrieval
Document Score Distribution Models for Query Performance Inference and Prediction
ACM Transactions on Information Systems (TOIS)
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Predicting query performance , that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. Our novel approach to addressing this challenge is based on estimating the potential amount of query drift in the result list, i.e., the presence (and dominance) of aspects or topics not related to the query in top-retrieved documents. We argue that query-drift can potentially be estimated by measuring the diversity (e.g., standard deviation) of the retrieval scores of these documents. Empirical evaluation demonstrates the prediction effectiveness of our approach for several retrieval models. Specifically, the prediction success is better, over most tested TREC corpora, than that of state-of-the-art prediction methods.