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

2024-11-25

arXiv.org

AI Summary

Current recommender system research overemphasizes model complexity and computational cost, remains confined to narrow domains (e.g., e-commerce and media), and lacks rigorous real-user evaluation and societal impact validation. To address these limitations, we propose RS4Good—a paradigm shift toward applying recommendation technology to high-impact societal domains such as healthcare, education, and sustainability. Our method centers on a human-centric, interdisciplinary empirical framework integrating human-in-the-loop evaluation, co-design with domain experts, real-world deployment, and longitudinal user studies. We de-emphasize pure accuracy metrics and explicitly model social impact. Furthermore, we systematically curate reproducible, cross-domain case studies and establish evaluable, sustainable criteria for socially responsible research. RS4Good aims to realign the research community’s priorities, strengthen the scientific credibility of recommender systems, and amplify their tangible societal contribution.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Recommender Systems
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

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.

Problem

• Recommender systems focus too narrowly on e-commerce and media.
• Lack of user evaluation and practical application in current research.
• Need for recommender systems that address societal good (RS4Good).

Innovation

• Recommender systems for societal good (RS4Good)
• Interdisciplinary collaborations for research
• Longitudinal evaluation with human involvement