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

TL;DR

The paper identifies a gap between RS research that prioritizes offline accuracy in limited domains and the need for societal impact. It proposes RS4Good as a paradigm shift—combining interdisciplinary collaboration, longitudinal human-in-the-loop evaluation, and a focus on real-world societal benefits. By surveying health, education, wellbeing, societal development, and environmental use cases, it highlights opportunities and the challenges of moving beyond offline metrics. The authors advocate for slower, more impactful science, new publication practices, and dedicated venues to foster research that matters for society.

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.

Figures (1)

• Figure 1: Selected RS4Good Use Cases