With the growing dominance of online platforms in the media, creative, and cultural industries (MCCI), recommender systems have become omnipresent in MCCI, from production to distribution and consumption. These systems leverage training data, algorithms, and prediction models to deliver automated, flexible, and immediate responses tailored to the personal preferences of content consumers. By filtering, ranking, and eventually recommending content to users, recommender systems influence exposure in two ways: they can promote items that might otherwise go unnoticed, or they can hide, and thus virtually remove, other items. In both cases, algorithms are not neutral, raising important questions about how they are designed and implemented, who decides, and on what basis (Kunaver & Požrl, 2017).

The increasing role that recommender systems play in MCCI has fueled academic and societal debate (Ranaivoson et al., 2024). Designed to make citizens’ choices easier and better, these systems have been accused of encouraging polarization and radicalization, perpetuating and reinforcing real-world discriminations—notably linked to race and gender—and stifling minority perspectives, local content, and emerging artists.

In this context, the proliferation of recommender systems in MCCI raises two main sets of questions: those concerning their implementation, and those concerning their impact. First, there is a lack of research on the design and deployment of recommender systems in MCCI. For example: Why do companies and organizations decide to integrate recommender systems into their strategies and services? To what extent do content creators adapt their practices to the influence of such systems? Are consumers aware of these systems? How impactful are the design aspects of such systems on their overall usage?

Second, the impact of recommender systems remains largely unexplored, mainly due to conceptual and methodological reasons. Carrying biases in their design and implementation, these systems have the potential to impact diversity, transparency, and fairness—though each of these concepts is itself muti-faceted. Moreover, analyzing this impact requires access to data that is difficult for most researchers to obtain.

Against this background, this Research Topic encourages manuscripts based on original research, with themes of particular interest including, but not limited to:

• impact of recommender systems on practices and strategies
• recommender systems and diversity
• recommender systems and fairness
• algorithmic biases
• recommender systems and serendipity
• multi-stakeholder perspective to analyze recommender systems
• tech-editorial gaps in the deployment of recommender systems
• transparency in the deployment of recommender systems
• the use and impact of artificial intelligence on recommender systems
• regulation of recommender systems.

This Research Topic welcomes manuscripts in the fields of the social sciences (communication science, sociology, economics, information science, political science, etc.), human-computer interaction studies, the digital humanities, and related disciplines. Interdisciplinary approaches are particularly welcome.

Information and Instructions for Authors

This Research Topic accepts all article types listed below, except for Editorials (which may only be submitted by the Topic Editors).

Authors are encouraged to submit a manuscript summary before submitting a full manuscript. The deadline for manuscript summaries is 31 October 2025. A manuscript summary is simply a summary/outline of one's planned submission. The Topic Editors will review all manuscript summaries and provide feedback that authors should consider when writing their full manuscript. Manuscript summaries will not be published externally and there is no associated fee. The maximum word count for manuscript summaries is 2000 words.

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Recommender Systems in the Media, Creative, and Cultural Industries