1 Recommender systems in action

The chapter introduces recommender systems as the hidden machinery that orders modern digital life, from shopping and streaming to news, dating, and social media. It opens with the example of a person gradually pulled toward extreme conspiracy content, showing how user clicks and algorithmic suggestions reinforce each other in a feedback loop. Recommenders are presented as powerful tools for reducing information overload and helping users find relevant content, but also as systems whose effects can be difficult to separate from user choice, especially when their inner workings are opaque and driven by machine learning.

A central theme is that social media transformed from simple networked sharing spaces into algorithmically curated environments. The text distinguishes three models: subscription, where users see only content from people they follow; network, where resharing lets content travel beyond the original audience; and algorithmic, where systems also insert out-of-network content based on predicted relevance or engagement. This shift increased convenience and discovery, but it also encouraged passive consumption, endless scrolling, and the prioritization of content that keeps people engaged rather than content that is necessarily useful, accurate, or healthy.

The chapter argues that recommendation is itself a form of amplification, because it determines which voices, ideas, and behaviors receive attention in a world where attention is scarce. That makes recommender systems deeply consequential for culture, markets, and democracy: they can elevate marginalized voices and help movements organize, but they can also spread misinformation, intensify polarization, reward emotionally charged content, and contribute to harmful offline outcomes. Because of these effects, the chapter emphasizes the importance of measuring amplification, while also noting that doing so is technically and conceptually difficult since the systems evolve over time, influence users as much as users influence them, and operate within powerful platform incentives.

The feedback loop between user and recommender systems. Users and recommender systems are in a mutual feedback loop, with the output of one serving as the input to the other. The output of the algorithm, the recommendations, is the input for the users. The users’ output, what they engage with, is a signal for the recommender. On top of that, both the user and the recommender system update their internal state. Users change their minds and evolve their preferences over time, while algorithms learn users’ preferences and try to align more with them.

Given a list of items, this recommender system reranks them according to a predefined metric, such as value to the user.

Various social media models. In the Subscription model, the content is seen only by users who explicitly follow the content producer, with no options or resharing. In the Network model, users can also reshare content from users they follow, enabling the distribution of content outside of the immediate network. Finally, in the Algorithmic model, an algorithm can add content to users’ feeds, even with no direct connection to the content itself or its author.

• Recommender systems are a powerful tool—and often underappreciated as a tool to order vast amounts of information for us. As a technology that pervades every application we interact with, RecSys have the power to influence our preferences in numerous domains, including highly consequential ones such as news consumption, dating choices, and financial decisions.
• Social media was created as a tool to connect people on the internet—at first free of commercial interest—and to build location-free communities around shared interests.
• The evolution of the internet has brought about more online platforms that have been able to connect an unprecedented number of people. Given the high running costs and the investors’ demands, platforms were nudged into finding ways of monetizing such efforts.
• Social media platforms began experimenting with recommender systems as a means to align business and customer interests. By explicitly indicating business goals and taking into account users’ behavior, platforms were able to serve more relevant content to users—which made the users be more active and spend more time on the platforms.
• The use of such algorithms raises important questions about algorithmic amplification, such as understanding which content is amplified more and why. Different types of platform designs enable various approaches to thinking about amplification.

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