Adaptive linear information retrieval models
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Measuring retrieval effectiveness based on user preference of documents
Journal of the American Society for Information Science
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
On the reuse of past optimal queries
SIGIR '95 Proceedings of the 18th 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
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Information filtering has become an important component of modern information systems due to significant increase in its applications. The objective of an information filtering is to classify/categorize documents as they arrive into the system. In this paper, we investigate an information filtering method based on steepest descent induction algorithm combined with a two-level preference relation on user ranking. The performance of the proposed algorithm is experimentally evaluated. The experiments are conducted using Reuters-21578 data collection. A micro-average breakeven effectiveness measure is used for performance evaluation. The best size of negative data employed in the training set is empirically determined and the effect of Rnorm factor on the learning process is evaluated. Finally, we demonstrate effectiveness of proposed method by comparing experimental results to other inductive methods.