Recommender Systems for Sustainability: Overview and Research Issues

Recommender systems can be powerful tools for sustainability: The paper demonstrates that AI-powered recommendation technologies can help organizations and individuals make choices aligned with the UN's Sustainability Development Goals (SDGs), which aim to address poverty, environmental protection, and social prosperity.

Explainability is crucial for responsible recommendations: The paper emphasizes that recommender systems integrate explainable AI (XAI) alongside machine learning, meaning users can understand why a particular sustainable option is being recommended to them—an important safeguard against opaque algorithmic decision-making.

Multiple AI approaches work together for sustainability goals: Rather than relying on a single technology, effective sustainability-focused recommender systems combine machine learning, case-based reasoning, and constraint solving to evaluate options and find genuinely relevant alternatives that balance user needs with environmental or social impact.

The field is still developing: The paper identifies this as an emerging area with "open issues for future research," suggesting that while recommender systems show promise for supporting sustainability, best practices and standards for responsible implementation are still being established.

Recommendations must be user-relevant, not just sustainable: The paper stresses that systems find "user-relevant alternatives," indicating that sustainable recommendations are only effective if they actually meet people's needs—a reminder that responsible AI for sustainability requires balancing environmental goals with practical human considerations.