# Digital Nudging with Recommender Systems: Survey and Future Directions

arXiv: https://arxiv.org/abs/2011.03413 · HTML: https://mlsanity.com/paper/2011.03413/

## Key facts

- Cited by 16 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [Estimating and Penalizing Induced Preference Shifts in Recommender Systems](https://mlsanity.com/paper/2204.11966/) (2022)
- [Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI](https://mlsanity.com/paper/2203.10525/) (2022)
- [Algorithmic Assistance with Recommendation-Dependent Preferences](https://mlsanity.com/paper/2208.07626/) (2022)
- [PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization](https://mlsanity.com/paper/2603.19649/) (2026)
- [Towards Psychologically-Grounded Dynamic Preference Models](https://mlsanity.com/paper/2208.01534/) (2022)
- [Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging](https://mlsanity.com/paper/2309.01026/) (2023)
- [Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters](https://mlsanity.com/paper/2411.16645/) (2024)
- [From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agents](https://mlsanity.com/paper/2604.20065/) (2026)
- [Ads that Stick: Near-Optimal Ad Optimization through Psychological Behavior Models](https://mlsanity.com/paper/2509.20304/) (2025)
- [An Audit Framework for Adopting AI-Nudging on Children](https://mlsanity.com/paper/2304.14338/) (2023)
- [BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations](https://mlsanity.com/paper/2307.02797/) (2023)
- [Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation](https://mlsanity.com/paper/2507.18756/) (2025)
- [Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation](https://mlsanity.com/paper/2603.05780/) (2026)
- [Trading Engagement for Sustainability: Carbon-Aware Re-ranking for E-commerce Recommendations](https://mlsanity.com/paper/2606.04550/) (2026)
- [Personal Care Utility: Health as Everyday Infrastructure](https://mlsanity.com/paper/2606.14145/) (2026)
- [Nudging Sustainable Choices through LLM-Generated Recommendation Explanations](https://mlsanity.com/paper/2607.25726/) (2026)

## Abstract (arXiv)

Recommender systems are nowadays a pervasive part of our online user experience, where they either serve as information filters or provide us with suggestions for additionally relevant content. These systems thereby influence which information is easily accessible to us and thus affect our decision-making processes though the automated selection and ranking of the presented content. Automated recommendations can therefore be seen as digital nudges, because they determine different aspects of the choice architecture for users. In this work, we examine the relationship between digital nudging and recommender systems, topics that so far were mostly investigated in isolation. Through a systematic literature search, we first identified 87 nudging mechanisms, which we categorize in a novel taxonomy. A subsequent analysis then shows that only a small part of these nudging mechanisms was previously investigated in the context of recommender systems. This indicates that there is a huge potential to develop future recommender systems that leverage the power of digital nudging in order to influence the decision-making of users. In this work, we therefore outline potential ways of integrating nudging mechanisms into recommender systems.