Recommender systems for sustainability: overview and research issues.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Frontiers in Big Data Pub Date : 2023-10-30 eCollection Date: 2023-01-01 DOI:10.3389/fdata.2023.1284511
Alexander Felfernig, Manfred Wundara, Thi Ngoc Trang Tran, Seda Polat-Erdeniz, Sebastian Lubos, Merfat El Mansi, Damian Garber, Viet-Man Le
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引用次数: 0

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.

可持续发展的推荐系统:概述和研究问题。
可持续发展目标(sdg)被认为是一项普遍的行动呼吁,其总体目标是保护地球,消除贫困,确保所有人的和平与繁荣。为了实现这些目标,不同的人工智能技术发挥了重要作用。具体来说,推荐系统可以为组织和个人提供支持,以实现定义的目标。推荐系统集成了人工智能技术,如机器学习、可解释的人工智能(XAI)、基于案例的推理和约束解决,以便从潜在的大量选项中找到并解释与用户相关的替代方案。在本文中,我们总结了应用推荐系统来支持实现可持续发展目标的最新进展。在此背景下,我们讨论了未来研究的开放性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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