------------------------------------------------------------------------ *Please forward to anyone who might be interested*
Apologies for cross-posting. ------------------------------------------------------------------------ In collaboration with Frontiers in Big Data's special section in Recommender Systems, we are bringing researchers together to contribute to a Research Topic on: *Human Issues in Recommender Systems* Topic Editors: * Dr. Bruce Ferwerda, Jönköping University, Jönköping, Sweden * Dr. Christine Bauer, Utrecht University, Utrecht, The Netherlands https://www.frontiersin.org/research-topics/23412/human-issues-in-recommender-systems Submissions are ongoing with the final submission deadline for manuscripts to be considered on 31 January 2022. ------------------------------------------------------------------------ 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). For more information about the Research Topic, information on manuscript preparation, and related matters, please see: https://www.frontiersin.org/research-topics/23412/human-issues-in-recommender-systems Although the deadline for submission of manuscripts to this Research Topic is 31 January 2022, papers will be reviewed and published as they are received. Submitting an abstract before submitting the manuscript is encouraged but not required. We are looking forward to your contribution to the Research Topic. Sincerely yours, Dr. Bruce Ferwerda Dr. Christine Bauer (Topic Editors)
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