Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
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
The effect of accessing nonmatching documents on relevance feedback
ACM Transactions on Information Systems (TOIS)
Learning routing queries in a query zone
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Local Feedback in Full-Text Retrieval Systems
Journal of the ACM (JACM)
SIGIR '00 Proceedings of the 23rd 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
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
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR 2004 workshop: RIA and "where can IR go from here?"
ACM SIGIR Forum
Context-sensitive information retrieval using implicit feedback
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
Improve retrieval accuracy for difficult queries using negative feedback
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Mining term association patterns from search logs for effective query reformulation
Proceedings of the 17th ACM conference on Information and knowledge management
Query Expansion Using External Evidence
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Approximating true relevance distribution from a mixture model based on irrelevance data
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A Quantum-Based Model for Interactive Information Retrieval
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Mining Negative Relevance Feedback for Information Filtering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Feedback-driven result ranking and query refinement for exploring semi-structured data collections
Proceedings of the 13th International Conference on Extending Database Technology
Mining positive and negative patterns for relevance feature discovery
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
What can quantum theory bring to information retrieval
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
Exploration-exploitation tradeoff in interactive relevance feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploring a multidimensional representation of documents and queries
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Negative feedback: the forsaken nature available for re-ranking
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A pattern mining approach for information filtering systems
Information Retrieval
Active learning to maximize accuracy vs. effort in interactive information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A classification framework for disambiguating web people search result using feedback
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Negation for document re-ranking in ad-hoc retrieval
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Proceedings of the 20th ACM international conference on Information and knowledge management
Interactive sense feedback for difficult queries
Proceedings of the 20th ACM international conference on Information and knowledge management
Automatic query reformulation with syntactic operators to alleviate search difficulty
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)
A two-stage decision model for information filtering
Decision Support Systems
A model for mining relevant and non-redundant information
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Learning to rank from relevance feedback for e-discovery
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Query likelihood with negative query generation
Proceedings of the 21st ACM international conference on Information and knowledge management
Text mining in negative relevance feedback
Web Intelligence and Agent Systems
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Negative relevance feedback is a special case of relevance feedback where we do not have any positive example; this often happens when the topic is difficult and the search results are poor. Although in principle any standard relevance feedback technique can be applied to negative relevance feedback, it may not perform well due to the lack of positive examples. In this paper, we conduct a systematic study of methods for negative relevance feedback. We compare a set of representative negative feedback methods, covering vector-space models and language models, as well as several special heuristics for negative feedback. Evaluating negative feedback methods requires a test set with sufficient difficult topics, but there are not many naturally difficult topics in the existing test collections. We use two sampling strategies to adapt a test collection with easy topics to evaluate negative feedback. Experiment results on several TREC collections show that language model based negative feedback methods are generally more effective than those based on vector-space models, and using multiple negative models is an effective heuristic for negative feedback. Our results also show that it is feasible to adapt test collections with easy topics for evaluating negative feedback methods through sampling.