AI-POWERED PERSONALIZED TRAVEL RECOMMENDATION SYSTEM

Abstract

Travel planning is a complex, multi-criteria decision-making process that involves analyzing diverse factors such as budget constraints, travel duration, user preferences, weather conditions, geographical accessibility, and ongoing local events. Traditional trip planning methods require users to navigate multiple platforms— including weather services, travel booking websites, mapping applications, and event listing portals— resulting in increased cognitive load, time consumption, and suboptimal decision-making. These limitations highlight the need for an intelligent, unified, and automated solution. This paper presents an AI-powered Personalized Travel Recommendation System that leverages machine learning techniques and real-time data integration to deliver context-aware and user-centric travel recommendations. The system collects user-specific inputs such as budget, travel duration, interests (e.g., adventure, cultural, leisure), and destination preferences. These inputs are processed using a hybrid recommendation approach that combines content-based filtering, collaborative filtering, and rule-based decision mechanisms. The proposed system integrates multiple external APIs, including weather forecasting services, Google Maps for geospatial data and route optimization, and event-based APIs for identifying local festivals and activities. By applying data preprocessing, feature extraction, and ranking algorithms, the system generates optimized travel suggestions tailored to individual user profiles. Additionally, it dynamically constructs day-wise itineraries, performs cost estimation for transportation, accommodation, and activities, and generates intelligent travel checklists based on contextual factors such as weather and trip type. The system architecture follows a multi-layered design consisting of a presentation layer, application layer, AI recommendation engine, and data layer, ensuring scalability, modularity, and efficient data flow.