User Clustering

User clustering aims to group users with similar characteristics or behaviors, facilitating improved resource allocation and personalized services across diverse applications. Current research focuses on developing efficient clustering algorithms, including those leveraging deep learning, Bayesian nonparametric methods, and neural networks, often tailored to specific application domains like recommender systems, wireless communication networks, and resource management. These advancements improve the accuracy and scalability of user clustering, leading to enhanced personalization in various fields and more efficient resource utilization in complex systems. The impact spans improved recommendation systems, optimized network performance, and more effective resource allocation in industrial settings.