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Recommendation systems are essential tools that help users discover content they may be interested in, amidst the vast amount of information available online. However, current methods, such as using historical user-item interactions and collaborative filtering, have limitations in accurately predicting user preferences. Our research aims to address these challenges and improve the performance of recommendation systems. In this article, we propose a new approach to recommendation systems using a method called Probabilistic Matrix Factorization (PMF). We transform the standard PMF method into a communitybased PMF that takes into account implicit relationships between users and items. To achieve this, we use a machine learning technique called Reduced Kernel Extreme Learning Machine (RKELM). Our proposed framework is designed to integrate these implicit relationships and identify communities of users with similar preferences based on PMF. We conducted a comparative analysis of our newly developed model against existing methods, using two well-known datasets. Various performance metrics, such as prediction errors, were employed to evaluate the effectiveness of our proposed community-based PMF approach with RKELM. Our model demonstrates improved performance, achieving a 7% improvement for the Douban dataset and a 4% improvement for the Last.fm dataset. Despite the improvements demonstrated by our model, potential limitations and challenges may still exist, such as scalability to larger datasets or adaptability to different domains. Future work could explore these aspects and investigate further enhancements to our approach.

Recommendation systems are essential tools that help users discover content they may be interested in, amidst the vast amount of information available online. However, current methods, such as using historical user-item interactions and collaborative filtering, have limitations in accurately predicting user preferences. Our research aims to address these challenges and improve the performance of recommendation systems. In this article, we propose a new approach to recommendation systems using a method called Probabilistic Matrix Factorization (PMF). We transform the standard PMF method into a community-based PMF that takes into account implicit relationships between users and items. To achieve this, we use a machine learning technique called Reduced Kernel Extreme Learning Machine (RKELM). Our proposed framework is designed to integrate these implicit relationships and identify communities of users with similar preferences based on PMF. We conducted a comparative analysis of our newly developed model against existing methods, using two well-known datasets. Various performance metrics, such as prediction errors, were employed to evaluate the effectiveness of our proposed community-based PMF approach with RKELM. Our model demonstrates improved performance, achieving a 7% improvement for the Douban dataset and a 4% improvement for the Last.fm dataset. Despite the improvements demonstrated by our model, potential limitations and challenges may still exist, such as scalability to larger datasets or adaptability to different domains. Future work could explore these aspects and investigate further enhancements to our approach.