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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...
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With the rapid development of the internet, users tend to refer to the rating scores or review opinions on social platforms. Most recommendation systems use collaborative filtering (CF) methods to recommend items based on users’ ratings. The rating-based CF methods do not consider users’ review opinions on different aspects of items. The accuracy o...
Collaborative Filtering (CF) has become the standard approach to solve recommendation systems (RS) problems. Collaborative Filtering algorithms try to make predictions about interests of a user by collecting the personal interests from multiple users. There are multiple CF algorithms, each one of them with its own biases. It is the Machine Learning...
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... Poverty prediction has to be accompanied with approaches that help to counteract poverty. For example, [24] show how recommendation techniques can be applied to identify export diversification strategies in such a way that a country has a latent competitive advantage (when following this strategy). ...
... In the line of [24], recommendation services can be provided on the basis of the RCAScore of individual items. When applying collaborative filtering (CF), an item × RCAScore matrix summarizes the scores of items already exported by individual countries. ...
... In this simplified scenario, engaging in exporting solar equipment can be regarded also as a good idea for country 1 . For a detailed discussion of applying different CF algorithms in such application contexts, we refer to [24]. Furthermore, [62] discuss approaches to product diversification based on the concepts of social network analysis where relationships between countries and their products are analyzed for recommendation purposes. ...
Sustainability development goals (SDGs) are regarded as a universal call to action with the overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity for all people. In order to achieve these objectives, different AI technologies play a major role. Specifically, recommender systems can provide support for organizations and individuals to achieve the defined goals. Recommender systems integrate AI technologies such as machine learning, explainable AI (XAI), case-based reasoning, and constraint solving in order to find and explain user-relevant alternatives from a potentially large set of options. In this article, we summarize the state of the art in applying recommender systems to support the achievement of sustainability development goals. In this context, we discuss open issues for future research.
... In such a way, two products can be defined as close in the sense that they share many of the capabilities needed in order to export them in a competitive way. Co-occurrences based approaches have however a low predictive performance, and this fact favors machine learning approaches as better tools to measure relatedness both at country [18][19][20] and firm level [21,22]. In [16,23,24], the authors proposed approaches to explicitly model the relationship among products, capabilities, and development. ...
Tree-based machine learning algorithms provide the most precise assessment of the feasibility for a country to export a target product given its export basket. However, the high number of parameters involved prevents a straightforward interpretation of the results and, in turn, the explainability of policy indications. In this paper, we propose a procedure to statistically validate the importance of the products used in the feasibility assessment. In this way, we are able to identify which products, called explainers, significantly increase the probability to export a target product in the near future. The explainers naturally identify a low dimensional representation, the Feature Importance Product Space, that enhances the interpretability of the recommendations and provides out-of-sample forecasts of the export baskets of countries. Interestingly, we detect a positive correlation between the complexity of a product and the complexity of its explainers.
... These strategies allow to better cope with the relative scarcity of data and to learn more complex and effective models. Finally, we mention early attempts to adopt machine learning approaches in economic complexity (32)(33)(34), which however either lack the systematic comparison in prediction tasks we show here, use different data, discuss only very specific test cases, or propose methodologies which are not suitable for the type and amount of data relevant here. ...
Relatedness is a quantification of how much two human activities are similar in terms of the inputs and contexts needed for their development. Under the idea that it is easier to move between related activities than towards unrelated ones, empirical approaches to quantify relatedness are currently used as predictive tools to inform policies and development strategies in governments, international organizations, and firms. Here we focus on countries' industries and we show that the standard, widespread approach of estimating Relatedness through the co-location of activities (e.g. Product Space) generates a measure of relatedness that performs worse than trivial auto-correlation prediction strategies. We argue that this is a consequence of the poor signal-to-noise ratio present in international trade data. In this paper we show two main findings. First, we find that a shift from two-products correlations (network-density based) to many-products correlations (decision trees) can dramatically improve the quality of forecasts with a corresponding reduction of the risk of wrong policy choices. Then, we propose a new methodology to empirically estimate Relatedness that we call Continuous Projection Space (CPS). CPS, which can be seen as a general network embedding technique, vastly outperforms all the co-location, network-based approaches, while retaining a similar interpretability in terms of pairwise distances.
... Finally, although the literature has used mostly related ness density as an indicator, it is possible to estimate relatedness using other methods, such as latent factors for locations and activities in the latent comparative advantage method 172 , an Indian buffet process 173 or SVD 174 . ...
Economic complexity methods have become popular tools in economic geography, international development and innovation studies. Here, I review economic complexity theory and applications, with a particular focus on two streams of literature: the literature on relatedness, which focuses on the evolution of specialization patterns, and the literature on metrics of economic complexity, which uses dimensionality reduction techniques to create metrics of economic sophistication that are predictive of variations in income, economic growth, emissions and income inequality.
Economic complexity methods, and in particular relatedness measures, lack a systematic evaluation and comparison framework. We argue that out-of-sample forecast exercises should play this role, and we compare various machine learning models to set the prediction benchmark. We find that the key object to forecast is the activation of new products, and that tree-based algorithms clearly outperform both the quite strong auto-correlation benchmark and the other supervised algorithms. Interestingly, we find that the best results are obtained in a cross-validation setting, when data about the predicted country was excluded from the training set. Our approach has direct policy implications, providing a quantitative and scientifically tested measure of the feasibility of introducing a new product in a given country.
The relatedness between a country or a firm and a product is a measure of the feasibility of that economic activity. As such, it is a driver for investments at a private and institutional level. Traditionally, relatedness is measured using networks derived by country-level co-occurrences of product pairs, that is counting how many countries export both. In this work, we compare networks and machine learning algorithms trained not only on country-level data, but also on firms, which is something not much studied due to the low availability of firm-level data. We quantitatively compare the different measures of relatedness, by using them to forecast the exports at the country and firm level, assuming that more related products have a higher likelihood to be exported in the future. Our results show that relatedness is scale dependent: the best assessments are obtained by using machine learning on the same typology of data one wants to predict. Moreover, we found that while relatedness measures based on country data are not suitable for firms, firm-level data are very informative also for the development of countries. In this sense, models built on firm data provide a better assessment of relatedness. We also discuss the effect of using parameter optimization and community detection algorithms to identify clusters of related companies and products, finding that a partition into a higher number of blocks decreases the computational time while maintaining a prediction performance well above the network-based benchmarks.
A critical environmental problem in the Brazilian Amazon-rainforest region is the annual generation of 1.4 million tons of açaí seeds as a waste from açaí-pulp production. This work evaluates the techno-economic viability and environmental impacts of four alternatives to avail açaí seeds: complete combustion for power generation, integrated production of polyphenols and electricity, and co-production of mannose, polyphenols, and electricity by two different routes. Incineration is the least profitable alternative and requires long-term payback for the considered capacity of 6000 kg/h of seeds. The alternative that prescribes co-production of polyphenols and electricity has sound economic and environmental performances but misses the opportunity to avail the mannan content of açaí seeds to produce mannose. The comparison of mannose-producing processes unveiled lower capital and operational costs in the single-step oxalic acid hydrolysis than the sequential sulfuric acid and enzymatic hydrolysis, resulting in a payback time of 2.7 years and an internal rate of return of 34.8%. It further evinces lower aquatic toxicity and acidification potentials, but greenhouse gas emissions are slightly higher than in the sulfuric acid case. The biorefinery is shown as a viable and sustainable waste valorization pathway for açaí seeds, which could help develop a circular bioeconomy in the Amazon region.
The main purpose of the article is the forecasting the indicators of export of goods and services to the world economy using neural networks and testing the method on the example of a specific region of the Russian Federation.For data analysis, the authors use not the regression analysis, which is traditional for such purposes, but a neural network (implementation takes place using the Deductor program). The input and output parameters for the program are determined, an analytical table is compiled, predicted values are built. A feature of the analysis presented by the authors is that a forecast was made for the period, for which the data are already known to test the neural network. This allows evaluating how accurate the values produced by the program are.This article shows that the use of neural networks for export analysis is possible and realizable. The article describes the main subtleties and limitations of the possible application of the methodology. Also, the main directions of using this analysis and the possibilities of its application in practice are described. The obtained data are corresponded and close to reality. This indicates the accuracy of the constructed neural network.This method of forecasting exports and other indicators can be used by enterprises to adjust business plans and conduct SWOT and PEST analysis.KeywordsExportNeural networkIndicatorsAnalysisModelJEL CodesC55C61E27
