A Machine Learning-Based Tourism Recommendation System

With the rapid development of the tourism industry, providing efficient and personalized recommendation services for tourists has become an important issue to be solved. Traditional travel recommendation systems mostly rely on rule-based mechanisms or simple similarity calculations, which are difficult to meet the increasingly diverse personalized needs of users. In recent years, the rise of machine learning technology provides a more intelligent and accurate solution for personalized recommendation. Based on this, this paper constructs a travel recommendation system that integrates user information, destination characteristics and behavioral data, aiming to improve the quality of recommendation through a data-driven approach. The study not only focuses on the user's basic features and preferences, but also introduces text vectorization of destinations and multiple prediction models for the comparison and evaluation of personalized recommendation effects. The experimental results show that machine learning methods exhibit significant advantages in handling complex data structures and discovering potential preference patterns, especially the integrated models (e.g., XGBoost) outperform traditional models in terms of recommendation accuracy. The study concludes by pointing out the practical application value of the system and provides directions for further introduction of techniques such as sentiment analysis, reinforcement learning and multimodal data processing in the future, providing theoretical support and technical foundation for intelligent services in the tourism industry.