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 social 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 recommended 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. These experiments are based on ratings from random samplings of items and we discuss problems with previous experiments that employ skewed samples of user-selected examples to evaluate performance.

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In Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA, August 1999.

@InProceedings{mooney:SIGIR-99, title={Content-Based Book Recommending Using Learning for Text Categorization}, author={Raymond J. Mooney and Loriene Roy}, booktitle={Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation}, month={August}, address={Berkeley, CA}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:SIGIR-99", year={1999} }