SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
The use of MMR, diversity-based reranking for reordering documents and producing summaries
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
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Active feedback in ad hoc information retrieval
Proceedings of the 28th 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
Active relevance feedback for difficult queries
Proceedings of the 17th ACM conference on Information and knowledge management
Fast dynamic reranking in large graphs
Proceedings of the 18th international conference on World wide web
Exploration-exploitation tradeoff in interactive relevance feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Balancing exploration and exploitation in learning to rank online
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Ask me better questions: active learning queries based on rule induction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Active class selection for arousal classification
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
On bias problem in relevance feedback
Proceedings of the 20th ACM international conference on Information and knowledge management
A Survey of Automatic Query Expansion in Information Retrieval
ACM Computing Surveys (CSUR)
EGAL: exploration guided active learning for TCBR
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Active learning of combinatorial features for interactive optimization
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A split-list approach for relevance feedback in information retrieval
Information Processing and Management: an International Journal
Information Sciences: an International Journal
Active learning for protein function prediction in protein-protein interaction networks
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
An ensemble-clustering-based distance metric and its applications
International Journal of Business Intelligence and Data Mining
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Relevance feedback, which uses the terms in relevant documents to enrich the user's initial query, is an effective method for improving retrieval performance. An associated key research problem is the following: Which documents to present to the user so that the user's feedback on the documents can significantly impact relevance feedback performance. This paper views this as an active learning problem and proposes a new algorithm which can efficiently maximize the learning benefits of relevance feedback. This algorithm chooses a set of feedback documents based on relevancy, document diversity and document density. Experimental results show a statistically significant and appreciable improvement in the performance of our new approach over the existing active feedback methods.