Introduction Ranking and recommendation systems, also known as recommender systems and recommendation engines, are one of the primary ways that we navigate the deluge of information from products like YouTube, Facebook, Amazon, and TikTok. In this deluge, recommendation systems help allocate our finite time and attention across the zettabytes of data (trillions of gigabytes) now produced each year (Statista, 2021). They can be thought of as automated content curators, with their most obvious role being to choose a small set of items from a much larger set and show that small set to a particular person.
The key question that ranking answers: What is rewarded? 3 However, ubiquitous recommendation systems have far more significant societal impacts than simply choosing among a set of pre-existing items to display. Recommendation systems direct attention—and attention is a currency that can be converted into money, power, and status. Much of the focus of the current conversation around recommendation systems is around “algorithmic amplification” and the ways that they influence what content is consumed. However, even more important may be that recommendation systems can have enormous impacts on what content is produced.4
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This author finds the question of “What is rewarded?” as particularly crucial, as he sees it as one of the fundamental ‘compass questions’ around the impacts of social media and related technologies.
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The work of Lischka et al. (2021) and Tennenholtz et al. (2019) among others explore the game-theoretic dynamics of ranking and content production in much more detail.
Bridging-Based Ranking
