Travel Itinerary Generation using AI and Recommendation Systems

The rapid growth of artificial intelligence (AI) in the travel and tourism sector has revolutionized the way how tourists plans their trips. Personalized travel suggestions are provided by AI-powered systems, which streamlines the usually laborious process of organizing trips. This paper examines the integration of machine learning, natural language processing, and recommendation systems with a focus on AI-based travel itinerary generators. These programs can provide personalized itineraries by analyzing user preferences, including time, money, and hobbies. To improve personalization, methods including sentiment analysis (Logistic Regression), content and collaborative filtering, and K-Means clustering are used. Based on user’s input, Point of Interests (POIs) are filtered and ranked. Time slots are assigned within the itinerary and ordered to minimize travel time using K-means clustering algorithm. A hybrid of collaborative and content filtering is applied based on user feedback on which sentiment analysis is performed using logistic regression. The final itinerary is generated that includes suggested POIs with suggested start and end times. This paper illustrates how current developments in AI-driven travel solutions might improve user contentment and simplify travel arrangements. Notwithstanding these developments, issues like data privacy and personalization biases still need to be addressed in order to guide future studies.