Exploring AI-based Personalized Recommendation Systems on Korean Tourism Platforms: Focusing on Structures and Features

This study analyzes the structure and characteristics of AI-based personalized recommendation systems used by Korean tourism platforms. It categorizes platforms into three types—travel platforms, accommodation platforms, and local government smart tourism platforms—and compares how AI recommendation features are implemented in each category. Travel platforms fall into two primary forms: comprehensive platforms (e.g., Yanolja) that integrate a diverse set of recommendation functionalities, and specialized platforms that focus on specific recommendation features. Accommodation platforms exhibit clear differentiation: global online travel agencies (OTAs) offer multifaceted recommendation systems incorporating personalized algorithms, location-based recommendations, and popularity-based sorting, whereas domestic platforms generally adopt simpler approaches. Local government smart tourism platforms primarily provide basic recommendation features, with emphasis on general regional information rather than highly personalized recommendations. The study identifies multiple recommendation techniques—including user-based collaborative filtering, item-based collaborative filtering, purchase history-based recommendations, profile-based pre-trip questionnaires, popularity-based recommendations, location-based services, and Traveling Salesman Problem (TSP)-based route optimization—which are applied in different ways depending on platform objectives and user characteristics. This systematic analysis offers theoretical and practical implications for advancing personalized recommendation services within tourism platforms.