Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Using a generalized instance set for automatic text categorization
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Automatic Learning of User Profiles — Towards the Personalisation of Agent Services
BT Technology Journal
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
SIFT: a tool for wide-area information dissemination
TCON'95 Proceedings of the USENIX 1995 Technical Conference Proceedings
Dimensional reduction effects of feature vectors by coefficients of determination
CIS'04 Proceedings of the First international conference on Computational and Information Science
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In this work, we propose a new method for extracting user preferences from a few documents that might interest users. For this end, we first extract candidate terms and choose a number of terms called initial representative keywords (IRKs) from them through fuzzy inference. Then, by expanding IRKs and reweighting them using term co-occurrence similarity, the final representative keywords are extracted. Performance of our approach is heavily influenced by effectiveness of selection method for IRKs so we choose fuzzy inference because it is more effective in handling the uncertainty inherent in selecting representative keywords of documents. The problem addressed in this paper can be viewed as the one of finding a representative vector of documents in the linear text classification literature. So, to show the usefulness of our approach, we compare it with two famous methods - Rocchio and Widrow-Hoff - on the Reuters-21578 collection. The results show that our approach outperforms the other approaches.