An Adaptive Recommendation System for Personalized Stock Trading Advice Using Artificial Neural Networks

  • Kaensar, Chayaporn (Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang) ;
  • Chalidabhongse, Thanarat (Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang)
  • Published : 2005.06.02

Abstract

This paper describes an adaptive recommendation system that provides real-time personalized trading advice to the investors based on their profiles and trading information environment. A proposed system integrates Stochastic technical analysis and artificial neural network that incorporates an adaptive user modeling. The user model is constructed and updated based on initial user profile and recorded user interactions with the system. The information presented to each individual user is also tailor-made to fit the user's behavior and preference. A system prototype was implemented in JAVA. Experiments used to evaluate the system's performance were done on both human subjects and synthetic users. The results show our proposed system is able to rapidly learn to provide appropriate advice to different types of users.

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