A Hybrid Approach for Personalized and Intelligent Content Recommendation in Digital Library

The rapid digitization of cultural heritage materials has led to the substantial growth of digital library collections, particularly large and heterogeneous archives of periodicals. This expansion has intensified challenges related to content discovery, accessibility, and user engagement, as users increasingly struggle to navigate and identify relevant materials in periodical collections. In this context, intelligent interaction with cultural content has become an essential aspect of effectively accessing and utilizing resources in modern digital libraries, highlighting the need for adaptive and user-oriented mechanisms that support navigation and discovery. Artificial intelligence–driven personalization offers promising solutions to these challenges; however, digital library environments are often characterized by sparse interaction data, evolving user interests, and the continuous introduction of new resources, which limit the effectiveness of standalone content-based or collaborative approaches. This work proposes an integrated personalization approach that combines behavioral interaction data with semantic relationships between documents to support adaptive content delivery in digital libraries. The approach facilitates the discovery of both established and newly digitized or rarely accessed materials, supporting more effective access, exploration, and reuse of large and diverse digital library collections.