Search Results

You are looking at 1 - 9 of 9 items for :

  • "KNN implementation" x
Clear All
Ms. Natasha X Che
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.
Ms. Natasha X Che

set of training samples most similar to it. Different KNN implementations vary in terms of their choices of how the similarity between input vectors is calculated. In the present paper, the cosine similarity score is used as the similarity measure. The intuition behind the current paper’s product-based KNN implementation is simple– first look at what products a country already has a revealed comparative advantage in, and then recommend other products that are similar to those products. To explain the approach in more details, let’s first rewrite the RCA score

International Monetary Fund. Western Hemisphere Dept.

problems and is a popular approach in constructing recommender systems . The basic idea of KNN is learning by analogy-classifying the test sample by comparing it to the set of training samples most similar to it. Different KNN implementations vary in terms of their choices of how the similarity between input vectors is calculated. In the present paper, the cosine similarity score is used as the similarity measure. 20. The intuition behind the current papers product-based KNN implementation is simple . First the algorithm looks at what products a country already has a

Ms. Natasha X Che and Xuege Zhang

used methods in solving classification and pattern recognition problems, and is a popular approach in constructing recommender systems. The basic idea of KNN is learning by analogy– classifying the test sample by comparing it to the set of training samples most similar to it. Different KNN implementations vary in terms of their choices of how the similarity between input vectors is calculated. In the present paper, the cosine similarity score is used as the similarity measure. The intuition behind the product-based KNN implementation is simple– first look at what

Ms. Natasha X Che and Xuege Zhang
This paper studies the relationship between export structure and growth performance. We design an export recommendation system using a collaborative filtering algorithm based on countries' revealed comparative advantages. The system is used to produce export portfolio recommendations covering over 190 economies and over 30 years. We find that economies with their export structure more aligned with the recommended export structure achieve better growth performance, in terms of both higher GDP growth rate and lower growth volatility. These findings demonstrate that export structure matters for obtaining high and stable growth. Our recommendation system can serve as a practical tool for policymakers seeking actionable insights on their countries’ export potential and diversification strategies that may be complex and hard to quantify.
International Monetary Fund. Asia and Pacific Dept

sample by comparing it to the set of training samples most similar to it. Different KNN implementations vary in terms of their choices of how the similarity between input vectors is calculated. In our setting, the cosine similarity score is used as the similarity measure. The intuition behind the product-based KNN used for the export recommendation system is simple. First, it looks at what products a country already has a revealed comparative advantage in, and then recommends other products that are similar to those products. To explain the approach in more details

International Monetary Fund. Asia and Pacific Dept
After successfully weathering the pandemic in 2020, Brunei was hit by new waves of COVID-19, with case numbers going up significantly and new lockdown measures imposed in H2 2021. Reduced activities in mining and LNG manufacturing, combined with the negative impact of new pandemic variants on domestic services, led to a slowdown in the economy. Real GDP contracted by 1.6 percent in 2021. For 2022, growth is projected to rebound to 1.2 percent, on the back of easing of mobility constraints and a positive terms of trade shock due to surges in O&G prices. Inflation, while remaining relatively low at 2.2 percent at end 2021, has increased in 2022 and pressures are expected to remain elevated in the short term, owing to supply disruptions and higher food and fuel prices. The economy continues to diversify, with double-digit growth of the food/agriculture sector and a new fertilizer sector commencing production. The risks to the outlook are tilted to the downside, due to potential new COVID-19 variants, increased global uncertainty associated with an escalation of the war in Ukraine, monetary tightening from the US and a larger-than-expected growth slowdown in China. On the upside, higher energy prices would further improve the terms of trade and restore fiscal positions in the short term, while partially contributing to build the buffers needed to ensure stronger intergenerational equity. Strong policy actions are needed to boost medium-term growth and foster resilience.