  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.
