This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.

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Most work on intelligent information agents has thus far focused on systems that are accessible through the World Wide Web. As demanding schedules prohibit people from continuous access to their computers, there is a clear demand for information systems that do not require workstation access or graphical user interfaces. We present a personal news agent that is designed to become part of an intelligent, IP-enabled radio, which uses synthesized speech to read news stories to a user. Based on voice feedback from the user, the system automatically adapts to the user's preferences and interests. In addition to time-coded feedback, we explore two components of the system that facilitate the automated induction of accurate interest profiles. First, we motivate the use of a multistrategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the use of "concept feedback", a novel form of user feedback that is based on our agent's capability to construct explanations for the reasons that have led to a specific classification. Users can then critique these explanations which, from a machine learning perspective, allows for more direct changes to an induced concept than through the inclusion of additional training examples. We evaluate the proposed algorithms on user data collected with a prototype of our system, and assess the performance contributions of the system's individual components.

T T he invention of the movable type printing press launched the information age by making the mass distribution of information both feasible and economical. Newspapers, magazines, shopping catalogs, restaurant guides, and classified advertisements can trace their origins to the printing process. Five and a half centuries of technological progress in communications networks, protocols, computers, and user interface design led to the Web, online publishing, and e-commerce. Consumers and businesses have access to vast stores of information. All this information, however, used to be accessible only while users were tethered to a computer at home or in an office. Wireless data and voice access to this vast store allows unprecedented access to information from any location at any time.

dbillsus, pazzani, We describe a user interface for wireless information devices, specifically designed to facilitate learning about users' individual interests in daily news stories. User feedback is collected unobtrusively to form the basis for a content-based machine learning algorithm. As a resu'lt, the described system can adapt to users' individual interests, reduce the amount of information that needs to be transmitted, and help users access relevant information with minimal effort.