Clustering-Based Collaborative Filtering with an Incentivized and Penalized User Model

This research proposes a Clustering-Based Collaborative Filtering (CBCF) system that uses incentivized/penalized user models to improve recommendations and K-means clustering to classify users according to their preferences. The quality of recommendations is improved by grouping users according to common interests, offering rewards for constructive interactions, and penalizing inappropriate behavior. This strategy can boost user engagement by enhancing relevance and personalizing recommendations. Applications for CBCF are frequently discovered on streaming platforms and e-commerce, where user retention and satisfaction depend on adaptable, high-quality recommendations.