Since the Information Age came, a tremendous quantity of information has been made available online. The data may consist of general information, different fields of study, or even E-Commerce. This immense quantity of information often leads to a phenomenon known as information overload. The phenomenon led to the creation, development, and enhancement of different types of recommendation systems. Knowledge-Based Recommendation System (KBRS) suffers significantly in its performance since KBRS relies on user input and does not use other user preferences such as liked, visited, and trends. This study proposes an enhancement of the result retrieval process in the KBRS method that uses Case-Based Reasoning. The aim is to improve the recommendation process using Feature Weighting, Feature Normalization, and Weighted Cosine based on a study conducted by Knowledge/ Domain Experts in real-estate recommendation systems. The results demonstrate significant improvements in performance metrics such as Precision and NDCG, providing promising directions for future studies and practical implications in enhancing user satisfaction and engagement.