Personalization Travel Support Engine Using Reinforcement Learning

Abstract

In the tourist industry, there are tremendous websites which providing information about tourisms. Tourists who seek traveling information always spend a lot of time in searching for the relevant information. This paper proposes a prototype of Personalization Travel Support Engine by using reinforcement learning theory. The engine can learn user’s interests in the provided web site for travel information. The goal of this engine is to adaptively increase reward for interesting or relevant trip information and penalty for un interesting or non relevant trip information. The reinforcement learning is the algorithm to learn individual user’s profiles and analyze user’s interaction to the presented best trip information. The results from this study reveal that it is possible to develop Personalization travel support engine. As well, the information from this study provides useful information regarding the areas of personalization of web support system and e -Tourism.