Hybrid Property Post Recommendation System using K-Nearest Neighbors and Singular Value Decomposition

The rapid evolution of the real estate industry has highlighted the need for advanced recommendation systems that can assist users in navigating vast amounts of property data. This paper introduces a Hybrid Property Post Recommendation System, specifically designed for integration with platforms like TerraBKK. The system employs both K-Nearest Neighbors (KNN) and Singular Value Decomposition (SVD) techniques to deliver personalized property recommendations. By combining the strengths of content-based and collaborative filtering, this hybrid system enhances the relevance and accuracy of recommendations, addressing challenges such as data sparsity and the cold-start problem. The implementation includes a dual-model approach where results from both KNN and SVD are synthesized using a weighted average to optimize recommendation quality. The system's effectiveness is demonstrated using real estate data provided by Terra Media and Consulting Co., Ltd., showcasing its potential to significantly improve user engagement and decision-making in the real estate market.