T.-H. Yang and W.-P. Lee (Taiwan)
intelligent agent, personalization, machine learning, recommender system
It has been advocated to develop information appliances to provide ubiquitous Internet information access. However, the exponentially increasing information causes the problem of overloaded information. One way to overcome such a problem is to build intelligent recommender systems to retrieve what is really interests the user. By analyzing the information collected from the user, a personalized recommender system is able to reason his personal preferences and then provide the most appropriate information services to meet his needs. This paper presents an agent-based recommender system in which a decision tree-based approach is proposed to learn a user’s preferences. The experimental studies concentrate on how to recommend programmes to a user in the multi-media broadcasting environment, for example television. The results and analysis show the promise of our system.
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