Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
A multilevel approach to intelligent information filtering: model, system, and evaluation
ACM Transactions on Information Systems (TOIS)
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Automatic classification using supervised learning in a medical document filtering application
Information Processing and Management: an International Journal
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Modern Information Retrieval
Building a filtering test collection for TREC 2002
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Belief revision for adaptive information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Using bayesian priors to combine classifiers for adaptive filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic Pattern-Taxonomy Extraction for Web Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Mining Ontology for Automatically Acquiring Web User Information Needs
IEEE Transactions on Knowledge and Data Engineering
Deploying Approaches for Pattern Refinement in Text Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Combining multiple forms of evidence while filtering
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Utility-based information distillation over temporally sequenced documents
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Generating concise association rules
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A study of methods for negative relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A two-stage text mining model for information filtering
Proceedings of the 17th ACM conference on Information and knowledge management
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A pattern mining approach for information filtering systems
Information Retrieval
Text mining in negative relevance feedback
Web Intelligence and Agent Systems
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It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.