Enhancing countries’ fitness with recommender systems on the international trade network
- Liao, Hao National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, China
- Huang, Xiao-Min National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, China
- Wu, Xing-Tong National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, China
- Liu, Ming-Kai National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, China
- Vidmer, Alexandre National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, China
- Zhou, Ming-Yang National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, China
- Zhang, Yi-Cheng National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, China - Department of Physics, University of Fribourg, Switzerland
-
2018
Published in:
- Complexity. - 2018, vol. 2018, p. 5806827
English
Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries’ data of the International Trade Network. A recommendation system is then used to identify the products that correspond to the production capacity of each individual country but are somehow overlooked by the country. Then, we simulate the evolution of the country’s fitness if it would have followed the recommendations. The result of this work is the combination of these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Physique
- Language
-
- English
- Classification
- Physics
- License
- License undefined
- Identifiers
-
- RERO DOC 323482
- DOI 10.1155/2018/5806827
- Persistent URL
- https://folia.unifr.ch/unifr/documents/307508
Statistics
Document views: 56
File downloads:
- zha_ecf.pdf: 96