<p>Recommendation system technologies predominantly focus on user-item interaction data, which are mapped into shared vector spaces for digital representation. These representations are then analyzed to uncover the relationships between users and items. As recommendation technologies have seen widespread adoption, a novel challenge has emerged in supply-demand matching contexts: the dual-triangular recommendation problem, involving four key entities, i.e., users with their demands, and suppliers with their offered items, forming a heterogeneous information network. In this work, we introduce the concept of dual-triangular recommendation and formally define this scientific problem. We propose a dual-triangular recommendation algorithm, enhanced by large language models, which utilizes knowledge graph encoder and LLM-augmented encoder to generate embedding representations for the four entities. A multi-task framework is employed to enable the sharing of underlying parameters across multiple recommendation tasks within the dual-triangular context. Through extensive experiments conducted on a real-world technology commercialization platform dataset, patent transfer dataset, and talent recruitment dataset, we demonstrate the effectiveness of our approach, offering a feasible and scalable solution to the dual-triangular recommendation problem.</p>