A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Probabilistic retrieval based on staged logistic regression
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Inferring probability of relevance using the method of logistic regression
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
A framework for selective query expansion
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Proceedings of the 28th 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
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning a ranking from pairwise preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Combining fields for query expansion and adaptive query expansion
Information Processing and Management: an International Journal
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
A bayesian logistic regression model for active relevance feedback
Proceedings of the 31st 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
A comparative study of methods for estimating query language models with pseudo feedback
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
On identifying representative relevant documents
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Selected new training documents to update user profile
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A pattern mining approach for information filtering systems
Information Retrieval
A boosting approach to improving pseudo-relevance feedback
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Promoting divergent terms in the estimation of relevance models
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Is document frequency important for PRF?
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
A study on query expansion methods for patent retrieval
Proceedings of the 4th workshop on Patent information retrieval
A Survey of Automatic Query Expansion in Information Retrieval
ACM Computing Surveys (CSUR)
Fully utilize feedbacks: language model based relevance feedback in information retrieval
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
On modeling rank-independent risk in estimating probability of relevance
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Query phrase expansion using wikipedia in patent class search
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Learning to rank from relevance feedback for e-discovery
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
A hybrid model for ad-hoc information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Dynamically selecting an appropriate context type for personalisation
Proceedings of the sixth ACM conference on Recommender systems
Exploiting External Collections for Query Expansion
ACM Transactions on the Web (TWEB)
Fisher kernel based relevance feedback for multimodal video retrieval
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Ontology-based personalised retrieval in support of reminiscence
Knowledge-Based Systems
A Theoretical Analysis of Pseudo-Relevance Feedback Models
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Query-Performance Prediction Using Minimal Relevance Feedback
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Bias-variance analysis in estimating true query model for information retrieval
Information Processing and Management: an International Journal
Lexicon-based Document Representation
Fundamenta Informaticae - Cognitive Informatics and Computational Intelligence: Theory and Applications
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Relevance Feedback has proven very effective for improving retrieval accuracy. A difficult yet important problem in all relevance feedback methods is how to optimally balance the original query and feedback information. In the current feedback methods, the balance parameter is usually set to a fixed value across all the queries and collections. However, due to the difference in queries and feedback documents, this balance parameter should be optimized for each query and each set of feedback documents. In this paper, we present a learning approach to adaptively predict the optimal balance coefficient for each query and each collection. We propose three heuristics to characterize the balance between query and feedback information. Taking these three heuristics as a road map, we explore a number of features and combine them using a regression approach to predict the balance coefficient. Our experiments show that the proposed adaptive relevance feedback is more robust and effective than the regular fixed-coefficient feedback.