Book Recommending Using Text Categorization with Extracted Information

Abstract

Content-based recommender systems suggest documents, items, and services to users based on learning a profile of the user from rated examples containing information about the given items. Text categorization methods are very useful for this task but generally rely on unstructured text. We have developed a bookrecommending system that utilizes semi-structured information about items gathered from the web using simple information extraction techniques. Initial experimental results demonstrate that this approach can produce fairly accurate recommendations. Introduction There is a growing interest in recommender systems that suggest music, films, and other items and services to users (e.g. www.bignote.com, www.filmfinder.com) (Maes 1994; Resnik & Varian 1997). These systems generally make recommendations using a form of computerized matchmaking called collaborative filtering. The system maintains a database of the preferences of individual users, finds other users whose known preferenc..