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Recommender systems for sustainability: overview and research issues
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, r...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10642936/ https://www.ncbi.nlm.nih.gov/pubmed/37965497 http://dx.doi.org/10.3389/fdata.2023.1284511 |
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author | Felfernig, Alexander Wundara, Manfred Tran, Thi Ngoc Trang Polat-Erdeniz, Seda Lubos, Sebastian El Mansi, Merfat Garber, Damian Le, Viet-Man |
author_facet | Felfernig, Alexander Wundara, Manfred Tran, Thi Ngoc Trang Polat-Erdeniz, Seda Lubos, Sebastian El Mansi, Merfat Garber, Damian Le, Viet-Man |
author_sort | Felfernig, Alexander |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10642936 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106429362023-11-14 Recommender systems for sustainability: overview and research issues Felfernig, Alexander Wundara, Manfred Tran, Thi Ngoc Trang Polat-Erdeniz, Seda Lubos, Sebastian El Mansi, Merfat Garber, Damian Le, Viet-Man Front Big Data Big Data 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. Frontiers Media S.A. 2023-10-30 /pmc/articles/PMC10642936/ /pubmed/37965497 http://dx.doi.org/10.3389/fdata.2023.1284511 Text en Copyright © 2023 Felfernig, Wundara, Tran, Polat-Erdeniz, Lubos, El Mansi, Garber and Le. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Felfernig, Alexander Wundara, Manfred Tran, Thi Ngoc Trang Polat-Erdeniz, Seda Lubos, Sebastian El Mansi, Merfat Garber, Damian Le, Viet-Man Recommender systems for sustainability: overview and research issues |
title | Recommender systems for sustainability: overview and research issues |
title_full | Recommender systems for sustainability: overview and research issues |
title_fullStr | Recommender systems for sustainability: overview and research issues |
title_full_unstemmed | Recommender systems for sustainability: overview and research issues |
title_short | Recommender systems for sustainability: overview and research issues |
title_sort | recommender systems for sustainability: overview and research issues |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10642936/ https://www.ncbi.nlm.nih.gov/pubmed/37965497 http://dx.doi.org/10.3389/fdata.2023.1284511 |
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