Recommender systems suggest information sources and products to users based on learning from examples of their 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 recommended previously unrated items to users with unique interests and to provide explanations for its recommendations. Our work has focused on a content-based book recommending system called LIBRA. We have also explored combining our content-based approach and standard collaborative filtering.