Intelligent Agent for e-Tourism: Personalization Travel Support Agent using Reinforcement Learning
Anongnart Srivihok, Pisit Sukonmanee · 2005
Web personalization and one to one marketing have been introduced as strategy and marketing tools. By using historical and present information of customers, organizations can learn, predict customer's behaviors and develop products to fit potential customers. In this study, a Personalization Travel Support System is introduced to manage traveling information for user. It provides the information that matches the users’ interests. This system applies the Reinforcement Learning to analyze, learn customer behaviors and recommend products to meet customer interests. There are two learning approaches using in this study. First, Personalization Learner by Group Properties is learning from all users in one group to find the group interests of travel information by using given data on user ages and genders. Second, Personalization Learner by User Behavior: user profile, user behaviors and trip features will be analyzed to find the unique interest of each web user. The results from this study reveal that it is possible to develop Personalization Travel Support System. Using weighted trip features improve effectiveness and increase the accuracy of the personalized engine. Precision, Recall and Harmonic Mean of the learned system are higher than the original one. This study offers useful information regarding the areas of personalization of web support system.