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Related Experiment Video

Updated: Jul 11, 2025

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

Recommender systems for sustainability: overview and research issues.

Alexander Felfernig¹, Manfred Wundara², Thi Ngoc Trang Tran¹

¹Applied Software Engineering & AI, Institute of Software Technology, Graz University of Technology, Graz, Austria.

Frontiers in Big Data

|November 15, 2023

Summary

Recommender systems, using AI like machine learning, help achieve Sustainability Development Goals (SDGs). This review explores their current applications and future research directions for global progress.

Keywords:

artificial intelligence machine learning recommender systems sustainability sustainability development goals

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Area of Science:

Artificial Intelligence
Computer Science
Sustainability Science

Background:

The United Nations' Sustainability Development Goals (SDGs) provide a global framework for addressing critical challenges.
Achieving the SDGs requires innovative technological solutions to support diverse organizations and individuals.
Artificial Intelligence (AI) offers powerful tools for tackling complex sustainability issues.

Purpose of the Study:

To review the current state-of-the-art applications of recommender systems in supporting SDG achievement.
To highlight the role of AI technologies, including machine learning and explainable AI (XAI), within recommender systems for sustainability.
To identify and discuss open challenges and future research directions in this interdisciplinary field.

Main Methods:

Literature review of recommender systems applied to sustainability development goals.
Analysis of AI techniques integrated into recommender systems, such as machine learning, XAI, case-based reasoning, and constraint solving.
Synthesis of findings to map current applications and identify research gaps.

Main Results:

Recommender systems effectively leverage AI to present user-relevant alternatives for achieving specific SDGs.
Key AI technologies like machine learning and XAI are crucial for the functionality and explainability of these systems.
The application of recommender systems spans various SDGs, offering practical support for goal attainment.

Conclusions:

Recommender systems are a valuable AI-driven tool for advancing Sustainability Development Goals.
Further research is needed to explore novel AI approaches and address existing challenges in recommender system applications for sustainability.
Continued development in this area can significantly contribute to global efforts in planet protection, poverty eradication, and peace.