Content-based book recommending using learning for text categorization

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