Smart Tourism: Design and Application of Artificial Intelligence-Assisted Tourism Service Recommendation Algorithms

Abstract

Artificial intelligence (AI) is developing at a rapid pace, which has profound effects on several industries, including tourism. This paper explores the design and application of AI-assisted tourism service recommendation algorithms, a key component of smart tourism. Smart tourism leverages AI technologies to enhance the travel experience by providing personalized and efficient services. The research focuses on the change and implementation of machine-acquiring knowledge of algorithms and recommendation systems that examine enormous volumes of data such as user preferences, behaviour, historical travel patterns, to offer tailored travel suggestions. We discuss the integration of AI in various stages of the tourism lifecycle, from pre-trip planning and booking to on-site experiences and post-trip engagement. The research highlights the efficacy of content-based filtering, hybrid recommendation, and collaborative filtering models in improving the accuracy and relevance of recommendations. Case studies from leading smart tourism destinations illustrate the practical benefits and challenges of AI implementation in real-world scenarios. The findings suggest that AI-driven recommendations can significantly enhance user satisfaction, optimize resource allocation for service providers, and drive innovation in the tourism industry. Future directions include addressing privacy concerns, enhancing algorithm transparency, and ensuring equitable access to smart tourism benefits across diverse demographic groups.