A Case Base Travel Advisory System for Personalization

Abstract

Web personalization and one to one marketing have been introduced as strategy and marketing tools. By using historical and present information of customers, organization can learn, predicts customer’s behavior and develop services to fit potential customers. 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 in a Travel Advisory System (PTAS). In this study, a Personalization in Travel Advisory System (PTAS) is introduced to manage traveling information for users. It provides the information that matches the users’ interests. This system applies the Reinforcement learning to analyze, learn customer behaviors and recommended conditions to meet customer interests.