An adaptive stock tracker for personalized trading advice

  • Authors:
  • Jungsoon Yoo;Melinda Gervasio;Pat Langley

  • Affiliations:
  • Middle Tennessee State University, Murfreesboro, TN;Institute for the Study of Learning and Expertise, Palo Alto, CA;Institute for the Study of Learning and Expertise, Palo Alto, CA

  • Venue:
  • Proceedings of the 8th international conference on Intelligent user interfaces
  • Year:
  • 2003

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Abstract

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