A Systematic Literature Review of User Behavior and Personalization in Digital Libraries

This systematic literature review explores the intersection of user behavior and personalization in digital libraries. The digital library environment is shaped by diverse user needs and expectations, making understanding user behavior a critical factor in enhancing personalization. Personalization has emerged as a key strategy to increase user satisfaction, engagement, and resource discovery. However, studies addressing privacy, biases, and scalability in digital library personalization are still scarce. Moreover, there is also limited existing study focusing on ethical and user-friendly personalization. A total of 45 articles were selected from 720 initial records retrieved from databases including Scopus, Web of Science, Emerald Insight, and IEEE Xplore, using predefined inclusion and exclusion criteria and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study underscores the role of user behavior analytics and machine learning in personalizing digital libraries, highlighting challenges such as privacy, algorithmic biases, and scalability. It emphasizes balancing personalization with ethical considerations and offers actionable insights for librarians and developers to create user-centric systems.