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...

Data availability on the sustainability of products is low which poses challenges for actors from all sectors dealing with promoting sustainable consumption. We describe how we currently provide users of a Chrome browser extension with general sustainability advice and with recommendations of...

Recommender systems (RS) are ubiquitous in digital space. This paper develops a deep learning-based approach to address three practical challenges in RS: complex structures of high-dimensional data, noise in relational information, and the black-box nature of machine learning algorithms. Our...

Online Product Recommendation (OPR) systems offered by Ecommerce website are crucial in providing real-time productrecommendations based on customers’ past behavior and references,contributing a significant impact on increasing purchasers’ satisfaction,boosting websites’ revenue through...

We investigate the impact of collaborative filtering recommender algorithms (e.g., Amazon’s “Customers who bought this item also bought”) commonly used in e-commerce on sales diversity. We use data from a randomized field experiment run on the website of a top retailer in North America...