Content-Based Book Recommending Using Learning for Text Categorization

Details Content-Based Book Recommending Using Learning for Text Categorization Content-Based Book Recommending Using Learning for Text Categorization

1999-02-07

arxiv.org/abs/cs/9902011v1

Computer Science

Digital Libraries

8 pages, 3 figures, Submission to Fourth ACM Conference on Digital Libraries

Scientific paper

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.

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