Comparison of Decision Tree and Support Vector Machine Algorithm for Classification of Tourist Attraction Sites Based on User Context

Today, tourism and tourist attraction as one of the economic resources, as well as the study of tourism data, have become especially important given the growing importance of the tourism industry and the competitiveness of this industry. In the tourism industry, recognizing the characteristics and information of the user's context leads to more targeted decisions and more satisfactory service to the user, which is not possible without the use of datamining tools and techniques. There are several methods for categorizing and verifying data. Considering the importance of recognizing the behavior and characteristics of tourists in choosing a tourist attraction place and thus attracting tourists' satisfaction, the aim of this study is to compare two decision tree and Support Vector Machine algorithms for categorizing tourist attraction sites based on user context information in Weka software. In this regard, user context information such as age, gender, educational level, type of tourist site and the point that users have given to the tourist destination has been used to classify tourist attraction sites. For this purpose, the user context and information of tourist place from 220 users were collected in Tehran tourist attractions and used for training and testing of two algorithms. By examining the results of this research, it was determined by different criteria that the decision tree has a better performance than the Support Vector Machine on the data used.