A personal news agent that talks, learns and explains

Abstract

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.