Recommender Systems for Sustainability: Overview and Research Issues

TL;DR

This paper reviews how recommender systems, integrating AI technologies like machine learning and explainable AI, support sustainability goals by providing relevant options and explanations, highlighting current research and future challenges.

Contribution

It offers a comprehensive overview of the application of recommender systems in sustainability, identifying key research issues and future directions.

Findings

Recommender systems aid in achieving SDGs by suggesting relevant options.

Explainable AI enhances transparency in sustainability recommendations.

Open research issues include scalability and ethical considerations.

Abstract

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.