Akademik Veri Yönetim Sistemi
Computational Intelligence, cilt.41, sa.5, 2025 (SCI-Expanded)
There has been an increasing interest in using GNNs to build recommender systems as they enable the representation of complex relationships between users and items through knowledge graph embeddings. However, most of the knowledge-graph-based systems focus only on ratings or reviews to build relationships. This prevents a comprehensive understanding of structural and positional information within graph data as well as user preferences that can change in time, as well. In order to address these issues, this paper aims to propose an advanced end-to-end Graph Neural Network architecture that significantly enhances recommendation system capabilities through the integration of state-of-the-art embedding techniques, knowledge graph frameworks, and transfer learning strategies. Incorporating positional encoding and topological feature extraction, the proposed model captures intricate user–item relationships and offers a robust representation that surpasses current approaches. A pretrained encoder facilitates knowledge transfer, effectively bridging domain gaps and amplifying prediction accuracy. Comprehensive evaluations against established baseline models reveal that our architecture has demonstrated enhanced accuracy, precision, and overall robustness. These results highlight the efficacy of combining knowledge graphs, sophisticated embedding strategies, and cross-domain transfer learning in building next-generation recommender systems, providing valuable insights for future advancements in the field.