Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
Not on the map. Why?
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 30 of its 137 references that are in the map. First posted on arXiv 25 Nov 2024.
Builds on
30 of 137 references.
References · 30
- 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
- 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
- 2019Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approachescited by 181
- 2020Neural Collaborative Filtering vs. Matrix Factorization Revisitedcited by 123
- 2022A Survey on the Fairness of Recommender Systemscited by 102
- 2019A Troubling Analysis of Reproducibility and Progress in Recommender Systems Researchcited by 65
- 2023A Survey on Popularity Bias in Recommender Systemscited by 42
- 2019Measuring the Business Value of Recommender Systemscited by 32
- 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
- 2021Fairness in Information Access Systemscited by 27
- 2022Building Human Values into Recommender Systems: An Interdisciplinary Synthesiscited by 22
- 2021Revisiting the Performance of iALS on Item Recommendation Benchmarkscited by 21
18 more
- 2022Trustworthy Recommender Systemscited by 21
- 2023Widespread Flaws in Offline Evaluation of Recommender Systemscited by 21
- 2023Bridging Systems: Open Problems for Countering Destructive Divisiveness across Ranking, Recommenders, and Governancecited by 17
- 2019Empirical Analysis of Session-Based Recommendation Algorithmscited by 16
- 2020Digital Nudging with Recommender Systems: Survey and Future Directionscited by 16
- 2020Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulationcited by 13
- 2021Designing Recommender Systems to Depolarizecited by 11
- 2024From Clicks to Carbon: The Environmental Toll of Recommender Systemscited by 11
- 2023The Effect of Third Party Implementations on Reproducibilitycited by 10
- 2020Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendationcited by 8
- 2024Advancing Sustainability via Recommender Systems: A Surveycited by 6
- 2024A Survey on Intent-aware Recommender Systemscited by 4
- 2024Revisiting BPR: A Replicability Study of a Common Recommender System Baselinecited by 3
- 2016Design of two combined health recommender systems for tailoring messages in a smoking cessation appcited by 1
- 2022Using consumer feedback from location-based services in PoI recommender systems for people with autismcited by 1
- 2022Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenshipcited by 1
- 2022Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Studycited by 1
- 2023Performance Comparison of Session-based Recommendation Algorithms based on GNNscited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.