Large amounts of tourism information on websites help individual tourists to plan their trips. At the same time, because the expansion of information overload problem, users have to invest a lot of time and effort to find satisfactory information or products. In this situation, a recommender system, which provides personalized predictions, is attracting attention in many E-commerce sites as one of the solution to reduce the problem. In this paper, we investigate the accuracy of traditional recommendation algorithms under several conditions using multi-agent simulation. Moreover, we propose a tourism information system with personalized recommendation using a new method of appropriately switching multiple recommendation algorithms.
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