Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Margin-based local regression for adaptive filtering
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Incremental profile learning based on a reinforcement method
Proceedings of the 2005 ACM symposium on Applied computing
Bayesian graphical models for adaptive filtering
Bayesian graphical models for adaptive filtering
A new approach to intelligent text filtering based on novelty detection
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
A new approach to intelligent text filtering based on novelty detection
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
A new nearest neighbor rule for text categorization
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Automated query learning with Wikipedia and genetic programming
Artificial Intelligence
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In this paper, we address the problem of adaptive document filtering. Traditionally, user profiles are represented by the centroid of the available examples, assuming that these are homogeneously distributed around this centroid. However, these examples may be irregularly distributed, being some areas more populated than others. While, in this case, the homogeneity assumption may not be globally true, it may still hold locally. In order to handle this phenomenon, we introduce a new approach in which a binary classifier for each user profile is used and more than one document is considered in the classification task. To decide whether a new document is relevant to the user or not, our approach uses a Nearest Neighbor classifier based on a neighborhood which inspects a sufficiently small area surrounding the new document. Experiments carried out on the TREC-11 collection show the effectiveness of the proposed method.