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.

翻译：在推荐系统领域，绝大多数研究工作致力于开发日益复杂的推荐模型，同时消耗越来越多的计算资源。遗憾的是，这些研究大多聚焦于极少数应用领域，主要是电子商务和媒体推荐。此外，许多模型从未经过用户评估，更不用说投入实际应用。因此，学者们这些努力的科学价值、经济价值和社会价值在很大程度上仍不明确。为使这些努力产生更积极的正面影响，我们认为研究界应更多关注推荐系统促进社会公益的应用场景（RS4Good）。在这篇观点性文章中，我们首先讨论了文献中已成功探索的、将推荐系统应用于社会关切问题的若干案例。随后，我们阐述了开展有效RS4Good研究所需的范式转变，其核心要素在于跨学科合作以及融入人类参与的长周期评估方法。