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

TL;DR

This paper surveys how recommender systems can be applied to societal good, highlighting successful use cases and advocating for interdisciplinary, human-in-the-loop research to maximize societal impact.

Contribution

It emphasizes the need for a paradigm shift towards interdisciplinary collaboration and longitudinal evaluation in RS4Good research, addressing a gap in societal impact.

Findings

Examples of recommender systems contributing to societal issues

Call for interdisciplinary collaborations in RS4Good

Advocacy for longitudinal, human-in-the-loop evaluation methods

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.