Codes and cryptography
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
CIKM '94 Proceedings of the third international conference on Information and knowledge management
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Learning routing queries in a query zone
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A method for obtaining digital signatures and public-key cryptosystems
Communications of the ACM
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Proceedings of the 2007 ACM symposium on Applied computing
Using the αβ-Neighborhood for Adaptive Document Filtering
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
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This paper presents an original approach to modelling user's information need in text filtering environment. This approach relies on a specific novelty detection model which allows both accurate learning of user's profile and evaluation of the coherency of user's behaviour during his interaction with the system. Thanks to an online learning algorithm, the novelty detection model is also able to track changes in user's interests over time.The proposed approach has been successfully tested on the Reuters-21578 benchmark. The experimental results prove that this approach signicantly outperforms the well-known Rocchio's learning algorithm.