1 Recommender systems in action

The chapter introduces recommender systems as the quiet machinery behind many everyday digital experiences, from shopping and streaming to social media and dating apps. It opens with a cautionary example of how a person can be gradually led from ordinary browsing into increasingly extreme and harmful content through a feedback loop between user behavior and algorithmic suggestions. The core point is that what users do and what the system recommends continuously shape one another, making it difficult to separate human choice from algorithmic influence.

It then explains that recommender systems are not limited to social media: they organize and prioritize content across the web to save users time and help platforms meet business goals. The chapter defines algorithms as instruction sets and recommender systems as algorithms that rank items according to some goal, often using machine learning to infer preferences from past behavior. Social media is presented as evolving from early subscription-based and network-based sharing into algorithmic feeds, where platforms increasingly curate not just the order of content but also what content enters the feed at all, including out-of-network material.

Finally, the chapter argues that recommendation is a form of amplification with serious societal consequences. Because attention is limited, ranking systems determine which voices and ideas gain visibility, which can reinforce popularity, shape culture, intensify emotions, and potentially spread misinformation or extremism. Measuring this amplification is presented as essential but difficult, since “value” is hard to define and algorithms are constantly changing. The chapter concludes that understanding these systems matters because their hidden design choices can influence users, creators, markets, and even democracy itself.

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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