The paper presents a content-based book recommending system, LIBRA (Learning Intelligent Book Recommending Agent), which uses machine learning for text categorization to make personalized recommendations. Unlike collaborative filtering methods that rely on other users' preferences, content-based methods use information about the item itself to make suggestions. LIBRA extracts book information from Amazon web pages, learns user profiles using a Bayesian learning algorithm, and ranks books based on these profiles. Initial experimental results on various book genres show that the system can produce accurate recommendations, even with limited training data. The paper also discusses the role of collaborative content in improving performance and suggests future research directions, including integrating content-based and collaborative approaches, using full-text content, and enhancing user interaction.The paper presents a content-based book recommending system, LIBRA (Learning Intelligent Book Recommending Agent), which uses machine learning for text categorization to make personalized recommendations. Unlike collaborative filtering methods that rely on other users' preferences, content-based methods use information about the item itself to make suggestions. LIBRA extracts book information from Amazon web pages, learns user profiles using a Bayesian learning algorithm, and ranks books based on these profiles. Initial experimental results on various book genres show that the system can produce accurate recommendations, even with limited training data. The paper also discusses the role of collaborative content in improving performance and suggests future research directions, including integrating content-based and collaborative approaches, using full-text content, and enhancing user interaction.