This paper presents a set of collaborative filtering algorithms that produce product recommendations to diversify and optimize a country's export structure in support of sustainable long-term growth. The recommendation system is able to accurately predict the historical trends in export content and structure for high-growth countries, such as China, India, Poland, and Chile, over 20-year spans. As a contemporary case study, the system is applied to Paraguay, to create recommendations for the country's export diversification strategy.
The paper quantifies these insights to characterize a country’s latent comparative advantages and produce exportdiversificationrecommendations, 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 exportdiversificationrecommendations 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