Which metropolitan city will innovate next?
This work compares matrix completion - a machine-learning technique often employed in the context of recommender systems - with a benchmark machine-learning model (random forest) to predict the future competitiveness of global cities in several technological areas based on a new patent dataset. Louvain community detection is employed as a pre-processing step to find similar innovation profiles of cities, facilitating the application of matrix completion and random forest. Results demonstrate that matrix completion reaches superior prediction performance as compared to the benchmark model. Our contribution shows how supervised machine learning can support local innovation and development policies.