# Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters

arXiv: https://arxiv.org/abs/2411.16645 · HTML: https://mlsanity.com/paper/2411.16645/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 137 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 30 of 30)

- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [Measuring the Business Value of Recommender Systems](https://mlsanity.com/paper/1908.08328/) (2019)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Fairness in Information Access Systems](https://mlsanity.com/paper/2105.05779/) (2021)
- [Building Human Values into Recommender Systems: An Interdisciplinary Synthesis](https://mlsanity.com/paper/2207.10192/) (2022)
- [Widespread Flaws in Offline Evaluation of Recommender Systems](https://mlsanity.com/paper/2307.14951/) (2023)
- [Revisiting the Performance of iALS on Item Recommendation Benchmarks](https://mlsanity.com/paper/2110.14037/) (2021)
- [Trustworthy Recommender Systems](https://mlsanity.com/paper/2208.06265/) (2022)
- [Digital Nudging with Recommender Systems: Survey and Future Directions](https://mlsanity.com/paper/2011.03413/) (2020)
- [Bridging Systems: Open Problems for Countering Destructive Divisiveness across Ranking, Recommenders, and Governance](https://mlsanity.com/paper/2301.09976/) (2023)
- [Empirical Analysis of Session-Based Recommendation Algorithms](https://mlsanity.com/paper/1910.12781/) (2019)
- [Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation](https://mlsanity.com/paper/2008.09472/) (2020)
- [Designing Recommender Systems to Depolarize](https://mlsanity.com/paper/2107.04953/) (2021)
- [From Clicks to Carbon: The Environmental Toll of Recommender Systems](https://mlsanity.com/paper/2408.08203/) (2024)
- [The Effect of Third Party Implementations on Reproducibility](https://mlsanity.com/paper/2307.14956/) (2023)
- [Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation](https://mlsanity.com/paper/2004.09980/) (2020)
- [Advancing Sustainability via Recommender Systems: A Survey](https://mlsanity.com/paper/2411.07658/) (2024)
- [A Survey on Intent-aware Recommender Systems](https://mlsanity.com/paper/2406.16350/) (2024)
- [Revisiting BPR: A Replicability Study of a Common Recommender System Baseline](https://mlsanity.com/paper/2409.14217/) (2024)
- [Design of two combined health recommender systems for tailoring messages in a smoking cessation app](https://mlsanity.com/paper/1608.07192/) (2016)
- [Using consumer feedback from location-based services in PoI recommender systems for people with autism](https://mlsanity.com/paper/2204.09969/) (2022)
- [Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship](https://mlsanity.com/paper/2208.01696/) (2022)
- [Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study](https://mlsanity.com/paper/2212.00592/) (2022)
- [Performance Comparison of Session-based Recommendation Algorithms based on GNNs](https://mlsanity.com/paper/2312.16695/) (2023)

## Built on by (most cited first, 2 of 2)

- [We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later](https://mlsanity.com/paper/2509.09414/) (2025)
- [Nudging Sustainable Choices through LLM-Generated Recommendation Explanations](https://mlsanity.com/paper/2607.25726/) (2026)

## Abstract (arXiv)

In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more computational resources. Unfortunately, most of these research efforts target a very small set of application domains, mostly e-commerce and media recommendation. Furthermore, many of these models are never evaluated with users, let alone put into practice. The scientific, economic and societal value of much of these efforts by scholars therefore remains largely unclear. To achieve a stronger positive impact resulting from these efforts, we posit that we as a research community should more often address use cases where recommender systems contribute to societal good (RS4Good). In this opinion piece, we first discuss a number of examples where the use of recommender systems for problems of societal concern has been successfully explored in the literature. We then proceed by outlining a paradigmatic shift that is needed to conduct successful RS4Good research, where the key ingredients are interdisciplinary collaborations and longitudinal evaluation approaches with humans in the loop.