Paper Title
Easy Trip: Personalized Trip Recommendation System

Abstract
At a time where tourists aspire to make better travel experiences while still on a tight budget, the creation of a web application tailored for the budget tourism consumer comes into fruition. This project aims at trying to solve this problem by developing a simple and user-friendly portal that the users can easily selecting the features to create their preferred travel itineraries all based on the budget constraint. The application includes a recommendation system based on machine learning and deep learning techniques like Matrix factorization which is used in collaborative filtering, which is a method that predicts a users interests by analyzing users preferences and K-means Clustering is used to group unlabeled data points into clusters to proffer places, lodging, and entertainment for every client, group, or budget. Given the features of comprehensive data analysis, real-time information integration, and user interaction mechanisms, the system will help to reduce the time and labor intensity of travel planning, raise user competence and enable people to have a meaningful and enjoyable travel. This short summary best captures the most salient aspects of our work, which sits on the transformative cusp of technologic enhancement and data-driven, affordable travel dreams. Keywords - Budget tourism, Travel planning, Recommendation systems, Matrix factorization, Collaborative filtering, K- means clustering, Machine learning, Deep learning, User experience, Travel itinerary optimization, Real-time data integration, Tourist decision support systems