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220928 ||| eng |
| 020 |
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|a 9781513555959
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| 100 |
1 |
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|a Che, Natasha
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| 245 |
0 |
0 |
|a Intelligent Export Diversification: An Export Recommendation System with Machine Learning
|c Natasha Che
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2020
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| 300 |
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|a 46 pages
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| 651 |
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4 |
|a Paraguay
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| 653 |
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|a Machine learning
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| 653 |
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|a Technological Change: Choices and Consequences
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| 653 |
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|a Income
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| 653 |
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|a Technology
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| 653 |
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|a Personal income
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| 653 |
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|a Export diversification
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| 653 |
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|a Trade: General
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| 653 |
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|a Intelligence (AI) & Semantics
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| 653 |
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|a Exports and Imports
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a International economics
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| 653 |
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|a Personal Income, Wealth, and Their Distributions
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| 653 |
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|a National accounts
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| 653 |
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|a Economic Development, Innovation, Technological Change, and Growth
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| 653 |
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|a International trade
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| 653 |
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|a Exports
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Comparative advantage
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| 653 |
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|a Neoclassical Models of Trade
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a IMF Working Papers
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| 028 |
5 |
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|a 10.5089/9781513555959.001
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| 856 |
4 |
0 |
|u https://elibrary.imf.org/view/journals/001/2020/175/001.2020.issue-175-en.xml?cid=49705-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 330
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| 520 |
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|a 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
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