Аннотация
With the exponential growth of scholarly publications, identifying relevant literature has become increasingly challenging for researchers. Traditional recommendation methods—such as collaborative filtering, content-based filtering, and citation analysis—often struggle to scale or capture deep semantic and structural relationships. In this study, we introduce DNGR (Deep Neural Graph-based Recommendation), a novel model that combines graph neural networks (GNNs) with SciBERT-based semantic embeddings to enhance scholarly paper recommendations. DNGR constructs a heterogeneous academic knowledge graph incorporating citation links, author collaborations, and topical associations. Each paper is represented by both its contextual semantics and structural position in the academic network. We evaluate DNGR on the AMiner v12 DBLP-Citation Network, comprising over 4.8 million papers and 45 million citation edges. Experiments using standard information retrieval metrics—Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (NDCG)—demonstrate that DNGR outperforms state-of-the-art-art baselines, achieving up to a 92% improvement in recommendation accuracy over methods like MSCN and Google Scholar. Our results highlight the potential of integrating deep contextual and graph-based signals for scalable and accurate citation recommendation. We also discuss limitations, ethical considerations, and potential extensions to multi-disciplinary datasets.