Graph-based technology recommendation system using GAT-NGCF

This study proposes a GAT-NGCF-based technology recommendation system to improve firms’ technological innovation capabilities and facilitate technology transfer. The system leverages Graph Attention Networks (GAT) to generate optimal representations of firms and patents, which are then applied in a firm–patent interaction graph using Neural Graph Collaborative Filtering (NGCF) to recommend the most suitable patents for transfer. Experiments conducted on 6,797 technology transfer and valuation cases demonstrated high performance, achieving a Recall@5 of 0.9984 and NDCG@5 of 0.9972. Notably, the proposed system outperformed State-Of-The-Art (SOTA) models in collaborative filtering, reinforcing its effectiveness. The system offers customized technology recommendations that align with firms’ technological needs and is expected to play a key role in supporting technology transfer and commercialization strategies through open innovation.