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Intelligent Export Diversification: An Export Recommendation System with Machine Learning

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  • Ms. Natasha X Che

Abstract

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.

Suggested Citation

  • Ms. Natasha X Che, 2020. "Intelligent Export Diversification: An Export Recommendation System with Machine Learning," IMF Working Papers 2020/175, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2020/175
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    Cited by:

    1. Tacchella, Andrea & Zaccaria, Andrea & Miccheli, Marco & Pietronero, Luciano, 2023. "Relatedness in the era of machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Andrea Tacchella & Andrea Zaccaria & Marco Miccheli & Luciano Pietronero, 2021. "Relatedness in the Era of Machine Learning," Papers 2103.06017, arXiv.org.
    3. Massimiliano Fessina & Giambattista Albora & Andrea Tacchella & Andrea Zaccaria, 2022. "Which products activate a product? An explainable machine learning approach," Papers 2212.03094, arXiv.org.
    4. Max Lugo Delgadillo, 2024. "The influence of femicide on criminal behavior: An empirical approach using economic complexity for crime prevention in Mexico/La influencia del feminicidio en el comportamiento criminal: un enfoque e," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 39(1), pages 121-157.

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