# Recommender Systems for Sustainability: Overview and Research Issues

arXiv: https://arxiv.org/abs/2412.03620 · HTML: https://mlsanity.com/paper/2412.03620/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 133 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Measuring the Business Value of Recommender Systems](https://mlsanity.com/paper/1908.08328/) (2019)
- [What are you optimizing for? Aligning Recommender Systems with Human Values](https://mlsanity.com/paper/2107.10939/) (2021)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects](https://mlsanity.com/paper/2102.07654/) (2021)
- [Market2Dish: Health-aware Food Recommendation](https://mlsanity.com/paper/2012.06416/) (2020)
- [Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards](https://mlsanity.com/paper/2007.09976/) (2020)
- [Matching Theory-based Recommender Systems in Online Dating](https://mlsanity.com/paper/2208.11384/) (2022)
- [KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting](https://mlsanity.com/paper/2102.07825/) (2021)
- [Towards a Recommender System for Profiling Users in a Renewable Energetic Community](https://mlsanity.com/paper/2209.05465/) (2022)

## Built on by (most cited first, 5 of 5)

- [Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendation](https://mlsanity.com/paper/2508.11105/) (2025)
- [We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later](https://mlsanity.com/paper/2509.09414/) (2025)
- [LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commerce](https://mlsanity.com/paper/2603.11025/) (2026)
- [Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure](https://mlsanity.com/paper/2604.26983/) (2026)
- [Trading Engagement for Sustainability: Carbon-Aware Re-ranking for E-commerce Recommendations](https://mlsanity.com/paper/2606.04550/) (2026)

## Abstract (arXiv)

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.