<p>This paper offers a new approach to implementing next-generation technology transfer strategies, policies and practices by conceptualising and testing the use of a professional social matching (PSM) system to recommend partnerships for innovation and technology transfer. Innovation is increasingly dependent on interactions between heterogeneous actors, as they produce and share technical knowledge. However, the identification of suitable partners is mostly unstructured and has not employed modern recommendation systems, nor has it accounted for the existence of multiscalar dynamics in innovation networks. After reviewing the most recent academic literature on PSM systems and innovation ecosystems, an original framework for a professional social matching for innovation and technology transfer (PSM4ITT) system was developed. Its applicability was tested on a sample of 2261 organisations innovating in photodegradation and photocatalyst technologies, with the US as a focus country. Key metrics to assess the impact of the PSM4ITT system were identified and tested using different design strategies and recommendation algorithms. It was found that the PSM4ITT system performed better when multiscalar information was an integral part of its design and recommendation algorithm, ultimately improving the positioning and cohesiveness of the US in the global network. Although limited to a proof-of-concept, this work enriches academia with first conceptualizations and results, opening new research opportunities for management and innovation scholars. Most importantly, it may guide the adoption of PSM4ITT systems, thus supporting policymakers in balancing openness and protectionism in times of change and uncertainty.</p>