Recommender Systems for Sustainability: Overview and Research Issues

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.

References in corpus (6)

• The role of artificial intelligence in achieving the Sustainable Development Goals
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Cited by in corpus (5)

• From Clicks to Carbon: The Environmental Toll of Recommender Systems
• Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
• Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
• Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender Systems
• From Query to Conscience: The Importance of Information Retrieval in Empowering Socially Responsible Consumerism