An adaptive stock tracker for personalized trading advice

The Stock Tracker is an adaptive recommendation system for trading stocks that automatically acquires content-based models of user preferences to tailor its buy and sell advice. The system incorporates an efficient algorithm that exploits the fixed structure of user models and relies on unobtrusive data-gathering techniques. In this paper, we describe our approach to personalized recommendation and its implementation in this domain. We also discuss experiments that evaluate the system’s behavior on both human subjects and synthetic users. The results suggest that the Stock Tracker can rapidly adapt its advice to different types of users. Categories and Subject Descriptors H.5 [Information Systems Applications]: Information Interfaces and Presentation General Terms Design, experimentation, human factors Keywords Adaptive user interfaces, machine learning, user modeling, personalization, information filtering

• Type: Conference
• Year: 2003
• Where: IUI
• Authors: Jungsoon P. Yoo, Melinda T. Gervasio, Pat Langley
• Keywords: IUI 2003, Software Engineering, Stock Tracker, Unobtrusive Data-gathering Techniques, User