Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.

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In Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195-204, San Antonio, TX, June 2000.

@InProceedings{mooney:dl00, title={Content-Based Book Recommending Using Learning for Text Categorization}, author={Raymond J. Mooney and Loriene Roy}, booktitle={Proceedings of the Fifth ACM Conference on Digital Libraries}, month={June}, address={San Antonio, TX}, pages={195-204}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:dl00", year={2000} }