Improved Decision Tree for Smart Tourism Service System based Big data

The growth in big data methods and Artificial Intelligence (AI) cause for radical transformation of tourism industries, resulted in smart tourism products and services. The services and products required for customizing to evolve requirements through generating exceptional experiences. In this research, proposed an Improved Decision Tree (IDT) method to smart tourist services and products depend on user experience and big data methods. The datasets used in the research for smart tourism are geographic information and user preference datasets. The data is processed by data cleaning, transformation and aggregation methods. Finally, IDT method is used for smart tourism service system. Performance of proposed algorithm is examined with performance metrics of accuracy, precision, recall and f1-score. Proposed algorithm obtained 0.9775 accuracy, 0.9683 precision, 0.9617 recall and 0.9537 f1-score on Geographic Information dataset. The proposed method obtained 0.9723 accuracy, 0.9682 precision, 0.9647 recall and 0.9605 f1-score on User Preference dataset. The proposed method performed effective than other existing techniques like Decision Tree (DT) and Collaborative filtering with attribute features.