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share. The paper quantifies these insights to characterize a country’s latent comparative advantages and produce export diversification recommendations, using machine learning algorithms that implement collaborative filtering , an approach used widely by online commercial applications for their recommender systems. A recommender system based on collaborative filtering uses the revealed preferences of a group of users to make predictions about the preferences of a user similar to the group. There are numerous applications of this approach in the e-commerce space
-specific diversification strategy, or practical insights in guiding the structural change in exports. In a recent study, Che (2020) proposes a novel method to operationalize the concept of comparative advantage and its evolution. It uses collaborative filtering algorithms in machine learning most commonly applied to product recommendations in e-commerce, to produce export diversification recommendations that reflect a country’s latent comparative advantages and future potentials in export structure. Section 3 will go over the details of the methodology. But the basic intuition