Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Ranking user's relevance to a topic through link analysis on web logs
Proceedings of the 4th international workshop on Web information and data management
Margin-based local regression for adaptive filtering
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
Automatic Pattern-Taxonomy Extraction for Web Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Deploying Approaches for Pattern Refinement in Text Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Combining multiple forms of evidence while filtering
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
An evaluation of adaptive filtering in the context of realistic task-based information exploration
Information Processing and Management: an International Journal
A two-stage text mining model for information filtering
Proceedings of the 17th ACM conference on Information and knowledge management
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Adaptive information filtering (AIF) is a challeng-ing issue for web search, as the Web contains non-structured data used by many different users. One of the main questions in AIF is how to keep the system up-to-date over time by increasing training on line with changes in the user’s needs and updating the parameters values accordingly. This paper investigates the use of Pattern Taxonomy Models (PTM) in adaptive filtering by adding an updating feature. We developed a mathematical model that updates training based on sliding windows over the positive and negative examples. Merging the scores of documents in the new windows with the old score of the system takes in to account the size of the training window and the type of document in each window. In order to test this approach, the mathematical model was implemented and tested with RCV1 data collection. The experimental results indicated that the new system improves performance of PTM.