Comprehensive study of Integrating Clustering and Adversarial Learning for Enhanced Recommender Systems: A Systematic Review of Hybrid Methodologies and Applications

Recommender systems (RSs) have become critical elements in modern instances of information and decision-support systems, resulting in a transformation of user experiences through highly personalized suggestions for an undeniably vast range of items. Although RSs have become commonplace, they continue to evolve, and their challenges, including sparsity, cold-start, scalability, and vulnerability to adversarial challenges remain. The use of clustering methods has proven highly effective in resolving these issues through the discovery and exploitation of latent user behaviour patterns, segmenting user groups that contribute more towards personalized and adaptable RSs. Additionally, adversarial learning has become a growing focus of study as a proposed solution for shielding and defending RSs, processes, and data from manipulation and attacks, resulting in greater resistance and trustworthiness. This study presents a systematic literature review (SLR) that explores the intersection of RSs, clustering methods, and adversarial learning. This paper synthesizes a critique of the latest hybrid recommendations, detailing motivations, challenges, directions for future study, and practical rec