Enhancing Tourism Recommendation System Using KNN-Based Hybrid Mobile Application

Tourism development in today's digital era much counts on technology in order to achieve convenience and easiness of travel. The design of a hybrid mobile application recommendation system using K-Nearest Neighbors algorithm, the KNN, improves the development of the tourists. This application is designed to monitor information on locations of tourists, hotel room reservations, booking a flight, booking of a taxi, and interests before use. It allows for real-time personal behavioral choices based on budget, time, and location information for the Recommendation system. KNN algorithm in identifying similar users and locations more properly and therefore leads to more appropriate and correct choices. A hybrid mobile application will provide the advantages of both a native and an online application, ensuring consistency and responsiveness irrespective of devices and operating systems. The mobile application is supposed to run on both the Android and iOS systems. Tests and evaluation results reveal that the system is effective in enhancing user engagement with destinations, satisfaction, and intentionality of revisiting, thereby underpinning its contribution to the sustainable development of tourism. This work contributes to the creation of a scalable user-centered strategy driven by insights and machine learning. This paper achieved the accuracy of 82.1 % on Kaggle dataset of restaurants available in Amsterdam. This approach encourages tourism, that is one of the main pillars of economy development of many countries.