1 Recommender systems in action

Recommender systems are presented as powerful tools that organize an overwhelming world of choices, from products and hotels to news, entertainment, social feeds, and dating. The chapter explains that these systems do more than simply help users find relevant content: they mediate attention, shape behavior, and create feedback loops in which users’ clicks, likes, pauses, and shares become signals that influence future recommendations. This mutual influence makes it difficult to separate human choice from algorithmic guidance, especially when recommendations gradually push users toward more extreme or harmful material.

The chapter defines recommender systems as algorithms that rank or select items according to a goal, often using machine learning based on past interactions rather than transparent rules. In social media, this ranking has evolved from simple chronological or subscription-based feeds to networked feeds powered by reshares, and then to fully algorithmic feeds that include content from people and topics users never chose to follow. This shift made platforms more engaging and commercially valuable, but it also gave algorithms a stronger role in deciding what users see, what goes viral, and which creators or viewpoints gain visibility.

The chapter emphasizes the societal consequences of recommendation and amplification. Social media can support activism, breaking news, and democratic participation, but the same systems can also spread misinformation, intensify polarization, reward emotionally charged content, and expose vulnerable users to harmful material. Because recommendation is a form of amplification, the chapter argues that measuring how algorithms distribute exposure is essential for accountability. It also highlights the power of large platforms, whose opaque systems and business incentives can influence culture, politics, public opinion, and even offline events at population scale.

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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