Using collaborative filtering to weave an information tapestry
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
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
Boosting and Rocchio applied to text filtering
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
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In linear text classification, user feedback is usually used to tune up the representative keywords (RK) for a certain class. Despite some algorithms (e.g. Rocchio) deal well with user positive and negative feedback to adjust the RKs, few researches have investigated how to adjust RKs only based on a small positive responses which is a popular case in the real-world application (e.g. users tend to click their interested URL). In this work, we describe a method of extracting representative keywords for a user from a small set of his positive feedback documents. Experiments on the Reuters-21578 collection illustrate that our approach is better than other two famous methods (Rocchio and Widrow-Hoff) with 24.8% and 14.5% improvement, respectively.