Hybrid Recommendation System for Property Bookings

About This Project

This project presents the development of a full-stack rental apartment platform enhanced with a hybrid recommendation system. The goal of the system is to improve the user experience by providing personalized property recommendations based on both user behavior and apartment characteristics. The platform allows users to create accounts, browse cities, view property listings, add apartments to a wishlist, and make bookings. User interactions such as views, wishlists, and bookings are collected and stored in the database to build behavioral profiles. To generate recommendations, the system combines collaborative filtering, which identifies patterns between users with similar behavior, and content-based filtering, which recommends properties with similar attributes such as location, price range, and amenities. By merging these two approaches into a hybrid model, the system reduces common challenges such as the cold-start problem and improves the overall relevance of recommendations. The project also demonstrates the integration of modern web technologies, backend services, and machine learning techniques to create a scalable recommendation pipeline within a real-world application scenario.