AI Chatbot for Tourist Recommendations: A Case Study in Vietnam

Abstract

Living standards are rising due to a more developed society, and recreation, particularly tourism, is becoming more critical. Expanding the tourist industry is one of the most significant concerns in economic growth. Tourism revenue has helped increase residents’ income, leading to socio-economic development. In recent years, emerging Vietnamese tourism spots like Hon Son, Sapa, Hue, Phu Quoc in Vietnam, and others have consistently drawn travellers to visit and experience through social networking platforms. Tourism potential is tremendous, but foreign visitors’ information about tourist destinations still needs to be improved. This work proposes an approach to integrating machine learning algorithms into an information system to consult tourism traveling. Machine learning algorithms can classify question topics, predict user intent, and predict conversation scenarios to give appropriate responses. Our method is evaluated on the dataset, including 7319 samples on 11 topics collected from the TWCS dataset, using three algorithms: Bag of Words, BERT, and RoBERTa. BERT achieved the highest performance among the surveyed algorithms with 90 % in accuracy and 90.1 % in F1-Score. From the trained model, the team built a mobile application on Android to deploy the chatbot application with the Flutter framework based on Dart, an object-oriented programming language developed by Google using the concept of containers. The system’s functionality serves two primary user groups: administrators and application users. Administrators can utilize the application’s primary functions to manage content set up, and train a chatbot. Users can access information about locations, read location articles, check hotel prices, and use chatbots to find answers to their location-related questions. Administrators can also train the chatbot model to expand its knowledge.

Keywords

• bert
• chatbot
• recommendation system
• tourist