About this Research Topic
Recommendation systems (or recommender systems) have become a pervasive ingredient in our everyday lives. Such systems assist people to navigate immense amounts of content. In doing so, recommenders help people find and discover various types of content and goods, including movies, music, books, food, or dating partners. When researching on, developing, or employing recommender systems, we have a social responsibility to care about the impact of their technology on individual people (this includes the roles of users, providers, and other stakeholders) and on society. This involves building, maintaining, evaluating, and studying recommender systems that are fair, transparent, and beneficial to society.
It is a combination of many aspects that make a recommender system successful. In this Research Topic, we zoom in on humans. The goal is to better understand humans' perceptions, needs, and the impact that recommender systems may have on humans. For instance, the call for fair and transparent recommenders is increasingly getting stronger. But what is fair? What is beneficial for society? And how can we achieve that? Early research in the field of fair and transparent fair recommender systems has been inspired by research in the machine learning domain, where we can observe a particular focus on the algorithmic perspective.
With this Research Topic, we want to show the bigger picture of the human issues, for instance, concerned with fair and transparent recommender systems. Recommender systems have an impact on individual people in the various roles they take (e.g., users, providers, and other stakeholders) and on society. What is fair and transparent from various perspectives? How can fairness and transparency be achieved? And how are the resulting recommendations perceived?
We welcome original research papers addressing human issues in recommenders, reporting research on theory and/or practice. The type of research may include but is not limited to, explorative studies, experiments, or methodological approaches studying human issues.
Topics of interest related to human issues include, but are not limited to, the following:
- Perception and expectations of stakeholders (e.g., users, providers);
- Human factors (e.g., humans-in-the-loop);
- Humanistic theory (e.g., philosophical, moral, and ethical analysis);
- Real-world cases and applications;
- Algorithmic development, measurement, and evaluation (e.g., bias and discrimination);
- Data (e.g., bias and discrimination).
It is a combination of many aspects that make a recommender system successful. In this Research Topic, we zoom in on humans. The goal is to better understand humans' perceptions, needs, and the impact that recommender systems may have on humans. For instance, the call for fair and transparent recommenders is increasingly getting stronger. But what is fair? What is beneficial for society? And how can we achieve that? Early research in the field of fair and transparent fair recommender systems has been inspired by research in the machine learning domain, where we can observe a particular focus on the algorithmic perspective.
With this Research Topic, we want to show the bigger picture of the human issues, for instance, concerned with fair and transparent recommender systems. Recommender systems have an impact on individual people in the various roles they take (e.g., users, providers, and other stakeholders) and on society. What is fair and transparent from various perspectives? How can fairness and transparency be achieved? And how are the resulting recommendations perceived?
We welcome original research papers addressing human issues in recommenders, reporting research on theory and/or practice. The type of research may include but is not limited to, explorative studies, experiments, or methodological approaches studying human issues.
Topics of interest related to human issues include, but are not limited to, the following:
- Perception and expectations of stakeholders (e.g., users, providers);
- Human factors (e.g., humans-in-the-loop);
- Humanistic theory (e.g., philosophical, moral, and ethical analysis);
- Real-world cases and applications;
- Algorithmic development, measurement, and evaluation (e.g., bias and discrimination);
- Data (e.g., bias and discrimination).
Keywords: Recommender systems, societal impact, fairness, transparency, bias, recommendations
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
