Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A language modeling approach to information retrieval
Proceedings of the 21st 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
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
The NRRC reliable information access (RIA) workshop
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Regularized estimation of mixture models for robust pseudo-relevance feedback
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An exploration of proximity measures in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Positional language models for information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Navigating in the Dark: Modeling Uncertainty in Ad Hoc Retrieval Using Multiple Relevance Models
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Adaptive relevance feedback in information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Reducing the risk of query expansion via robust constrained optimization
Proceedings of the 18th ACM conference on Information and knowledge management
A comparative study of methods for estimating query language models with pseudo feedback
Proceedings of the 18th ACM conference on Information and knowledge management
Finding good feedback documents
Proceedings of the 18th ACM conference on Information and knowledge management
Positional relevance model for pseudo-relevance feedback
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Robust ranking models via risk-sensitive optimization
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Modeling reformulation using query distributions
ACM Transactions on Information Systems (TOIS)
An incremental approach to efficient pseudo-relevance feedback
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback
Information Processing and Management: an International Journal
Bias-variance analysis in estimating true query model for information retrieval
Information Processing and Management: an International Journal
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Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback helps many queries, it also often hurts many other queries, limiting its usefulness in real retrieval applications. Thus an important, yet difficult challenge is to improve the overall effectiveness of pseudo-relevance feedback without sacrificing the performance of individual queries too much. In this paper, we propose a novel learning algorithm, FeedbackBoost, based on the boosting framework to improve pseudo-relevance feedback through optimizing the combination of a set of basis feedback algorithms using a loss function defined to directly measure both robustness and effectiveness. FeedbackBoost can potentially accommodate many basis feedback methods as features in the model, making the proposed method a general optimization framework for pseudo-relevance feedback. As an application, we apply FeedbackBoost to improve pseudo feedback based on language models through combining different document weighting strategies. The experiment results demonstrate that FeedbackBoost can achieve better average precision and meanwhile dramatically reduce the number and magnitude of feedback failures as compared to three representative pseudo feedback methods and a standard learning to rank approach for pseudo feedback.