Delivering services in digital libraries must align with users’ preferences, needs, and feedback. In this context, service personalization plays a pivotal role in enhancing user satisfaction and maximizing the utilization of digital library capacities. This study aims to systematically review the existing research on service personalization in digital libraries to identify key developments, research gaps, and future directions.
Employing a qualitative approach, the study follows the systematic review framework proposed by Kitchenham and Charters (2007). A total of 67 sources—including journal articles, conference papers, and master’s and doctoral theses—were identified and analyzed.
Findings reveal that the research landscape can be categorized into three main dimensions and nineteen subcategories: (1) user-centric services (including user profiles, user modeling and privacy, user behavior, collaborative environments, personal information spaces, contextual interoperability, user context awareness, user services, information security, cognitive styles, access methods, and “My Library” features); (2) types of personalization (covering personalization processes, indicators and methods, personalized search and results, recommender systems, and filtering); and (3) applied techniques and technologies (such as data mining, intelligent technologies, big data, and cloud computing). Among these, recommender systems received the most attention.
The evolution of research in this domain reflects a transition from foundational digital library infrastructure to advanced intelligent personalization, encompassing AI-driven behavior prediction, interest recognition, automated analysis, and customized service delivery. Despite notable progress, the study highlights the need for innovative and diverse research to address emerging challenges and technological shifts.