Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
This addresses the problem of limited societal impact in recommender systems research for the academic and broader community, proposing an incremental shift in research priorities.
The paper critiques the narrow focus of recommender systems research on e-commerce and media, advocating for a shift towards applications that contribute to societal good (RS4Good). It calls for interdisciplinary collaborations and longitudinal evaluations with humans to enhance the impact of research.
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.