A Review on Scholarly Publication Recommender Systems: Features, Approaches, Evaluation, and Open Research Directions

The exponential growth of scientific literature has made it increasingly difficult for researchers to identify relevant and timely publications within vast academic digital libraries. Although academic search engines, reference management tools, and recommender systems have evolved, many still rely heavily on metadata and lack mechanisms to incorporate full-text content or time-awareness. This review systematically examines the landscape of scholarly publication recommender systems, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology for a comprehensive and transparent selection of relevant studies. We highlight the limitations of current systems and explore the potential of integrating fine-grained citation knowledge—such as citation proximity, context, section, graph, and intention—extracted from full-text documents. These elements have shown promise in enhancing both the contextual relevance and recency of recommendations. Our findings highlight the importance of moving beyond accuracy-focused metrics toward user-centric evaluations that emphasise novelty, diversity, and serendipity. This paper advocates for the development of more holistic and adaptive recommender systems that better align with the evolving needs of researchers.