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

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.

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Publication Details

Journal
ACM Transactions on Recommender Systems
Published
2025-06-30
DOI
https://doi.org/10.1145/3746648
Citations
6
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
19.48
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article

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

Dietmar Jannach, Markus Zanker, Alan Said, Marko Tkalčič
6 citations
ACM Transactions on Recommender Systems
Recommender Systems and Techniques
19.48
article

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

Dietmar Jannach, Markus Zanker, Alan Said, Marko Tkalčič
article en
6 citations

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.

ACM Transactions on Recommender Systems
University of Primorska (SI), Free University of Bozen-Bolzano (IT), University of Klagenfurt (AT), University of Gothenburg (SE)
Openalex Percentile: Top 1%
Recommender Systems and Techniques
19.48
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