Personalized Tourism Recommendation Based on AI and Big Data Analytics

Abstract

The rapid development of artificial intelligence (AI) and big data technologies has transformed the landscape of tourism management, leading to the emergence of personalized recommendation systems that enhance user experience and destination competitiveness. This
study explores the design and implementation of a personalized tourism recommendation framework based on AI and big data analytics. By
collecting large-scale user behavior dataincluding browsing history, booking records, and preference attributesthis research employs machine learning algorithms such as collaborative filtering, clustering, and deep neural networks to generate accurate and adaptive recommendations. The performance of the proposed model is evaluated through precision, recall, and user satisfaction metrics, highlighting its effectiveness in predicting travelers' interests and improving engagement. In addition, the study discusses critical ethical and privacy issues associated
with AI-driven recommendation systems, proposing strategies for transparent and responsible data use. The findings contribute to the development of intelligent tourism ecosystems and offer practical implications for tourism enterprises aiming to optimize digital service experiences
and marketing strategies.