AUTHOR=Felfernig Alexander , Wundara Manfred , Tran Thi Ngoc Trang , Polat-Erdeniz Seda , Lubos Sebastian , El Mansi Merfat , Garber Damian , Le Viet-Man 

TITLE=Recommender systems for sustainability: overview and research issues

JOURNAL=Frontiers in Big Data

VOLUME=6

YEAR=2023

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2023.1284511

DOI=10.3389/fdata.2023.1284511

ISSN=2624-909X

ABSTRACT=<p>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.</p>