TOURISM RECOMMENDATION SYSTEM USING DECISION TREES FOR PERSONALIZED TRAVEL SUGGESTIONS

Travelers have the most difficult task before and during their trips: choosing a destination from all the available information, both online and otherwise. This issue was tried to be fixed in previous TRSs. Technical considerations, such as system accuracy, and practical considerations, such usability and satisfaction, have, however, received little attention from us. The first step in fixing this problem is learning all we can about visitors' decision-making processes and creating new models for their information-seeking behaviors. We provide a new kind of human-centered TRS in this research, which could lead visitors to hidden gems in an unfamiliar city. Using a real-world dataset, it considers both theoretical and practical aspects. A two-step feature selection method was used to limit the system's input count during development, and suggestions were made using decision tree C4.5. According to the research, the proposed TR Scan can successfully suggest vacation spots to every user.