Personalized Travel Recommendation System for Intelligent Tourism Planning

Abstract

Planning a trip can take a lot of time and work because travelers have to think about their own tastes and budget while also looking at a lot of different places, hotels, attractions, restaurants, ratings, and reviews. The tourism industry has become very data-driven because of the fast growth of digital information and social media sites. This makes it hard for people to quickly find the best travel options. This study examines a travel recommendation system that facilitates vacation planning through intelligent, tailored suggestions. The suggested system looks at things like the type of travel, the size of the group, the length of the stay, and the user's interests to suggest places to stay, eat, and visit. To make recommendations more accurate, it uses different methods, such as content-based filtering, collaborative filtering, and personalized approaches. The system also uses online reviews and travel-related data to make recommendations better. The travel recommendation system helps users make smart choices and makes planning trips easier by quickly processing a lot of tourism data. By connecting travelers with destinations that best match their interests and preferences, these kinds of systems can greatly improve the user experience, encourage tourism activities, and help the tourism industry grow.