The Use of Clustering Methods in Memory-Based Collaborative Filtering for Ranking-Based Recommendation Systems

This research explores the application of clustering techniques and frequency normalization in collaborative filtering to enhance the performance of ranking-based recommendation systems. Collaborative filtering is a popular approach in recommendation systems that relies on user-item interaction data. In ranking-based recommendation systems, the goal is to provide users with a personalized list of items, sorted by their predicted relevance. In this study, we propose a novel approach that combines clustering and frequency normalization techniques. Clustering, in the context of data analysis, is a technique used to organize and group together users or items that share similar characteristics or features. This method proves beneficial in enhancing recommendation accuracy by uncovering hidden patterns within the data. Additionally, frequency normalization is utilized to mitigate potential biases in user-item interaction data, ensuring fair and unbiased recommendations. The research methodology involves data preprocessing, clustering algorithm selection, frequency normalization techniques, and evaluation metrics. Experimental results demonstrate that the proposed method outperforms traditional collaborative filtering approaches in terms of ranking accuracy and recommendation quality. This approach has the potential to enhance recommendation systems across various domains, including e-commerce, content recommendation, and personalized advertising