Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Optimization of relevance feedback weights
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
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Text filtering by boosting naive Bayes classifiers
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the ninth international conference on Information and knowledge management
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
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
A reinforcement profile learning agent for documents filtering
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Inferring document utility via a decision-making based retrieval model
International Journal of Knowledge-based and Intelligent Engineering Systems
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This paper presents a profile learning method in an adaptive information filtering. The learning method is an incremental profile learning based on a reinforcement algorithm. The basic idea consists in building, when a document is selected and judged as relevant, the temporary profile which makes it possible to find this document with a strong score, then integrating this profile, using a logarithmic function, in the global profile. The proposed method is compared to two IR learning methods, query expansion method used in Okapi and Rocchio's algorithm. Experiments carried out on TREC1-2002 collection showed the effectiveness of the reinforcement method.Adaptive filtering, profile learning, reinforcement method