Modeling the Position of Using the Tourism Relationship Management System (TRM) Adopting Artificial Intelligence Methodology

For thousands of years, human beings have moved and travelled constantly for a range of reasons. These movements and travels are not typical of tourism today. Throughout the years, the composition, the time, the place, and the effects of travel have changed. Visiting another environment voluntarily is what is referred to as tourism. Tourism today is considered to be an important tool in identifying national and cultural identity, education, income, growth, and economic dynamism. An overview of the methods used in tourism recommendation systems, especially those that rely on user ratings for the generation of a proposal, is provided in this dissertation. To find similar users, these methods use similarity criteria between users. With more users and items in the system, the number of items that are commonly rated decreases, and group refining methods have difficulty finding users who are similar to the target user. Model-based method for categorizing users or items in the system is proposed using a new clustering method. In the proposed clustering method, users or items are classified into different clusters based on their trust relationships with other users. A comparison of the proposed method with other tested methods shows that based on the evaluation criteria used, it is more effective than the others.