Behind Recommender Systems: the Geography of the ACM RecSys Community

TL;DR

This paper examines the geographic diversity of the ACM RecSys research community over 15 years, emphasizing the importance of diverse perspectives in developing fair and inclusive recommender systems.

Contribution

It provides a long-term analysis of author affiliation countries in RecSys publications, highlighting geographic diversity trends and implications for inclusive AI development.

Findings

Geographic diversity among authors has increased over time.

Certain regions dominate the research community, indicating uneven global participation.

Diversity monitoring can inform more inclusive AI practices.

Abstract

The amount and dissemination rate of media content accessible online is nowadays overwhelming. Recommender Systems filter this information into manageable streams or feeds, adapted to our personal needs or preferences. It is of utter importance that algorithms employed to filter information do not distort or cut out important elements from our perspectives of the world. Under this principle, it is essential to involve diverse views and teams from the earliest stages of their design and development. This has been highlighted, for instance, in recent European Union regulations such as the Digital Services Act, via the requirement of risk monitoring, including the risk of discrimination, and the AI Act, through the requirement to involve people with diverse backgrounds in the development of AI systems. We look into the geographic diversity of the recommender systems research community, specifically by analyzing the affiliation countries of the authors who contributed to the ACM Conference on Recommender Systems (RecSys) during the last 15 years. This study has been carried out in the framework of the Diversity in AI - DivinAI project, whose main objective is the long-term monitoring of diversity in AI forums through a set of indexes.