1 Recommender systems in action

Recommender systems sit at the heart of today’s digital experiences, quietly ordering the overwhelming abundance of choices we face and concentrating attention on a tiny fraction of items. In social media, shopping, entertainment, news, and even dating, they personalize feeds to save time and surface what seems most relevant. Crucially, people and algorithms coevolve in a feedback loop: recommendations shape what we see and engage with, and our engagements train the next set of recommendations. This cycle can efficiently connect users to content—but it can also steer attention, norms, and beliefs, sometimes nudging behavior toward more extreme material through a process the chapter frames as algorithmic amplification.

The chapter explains recommender systems as ranking algorithms increasingly powered by machine learning and embedded across platform designs. Social media illustrates their rise: early chronological feeds gave way to algorithmic curation that prioritizes predicted engagement, shifting from subscription-only exposure to models that include network reshares and out-of-network content chosen by the system. The benefits are clear—less friction, tailored discovery, and scalable distribution—but so are the trade-offs: filter bubbles, cultural homogenization, addiction via intermittent rewards, creator dependence on opaque ranking shifts, and heightened risks for vulnerable users when harmful content is repeatedly reinforced. As gatekeeping erodes and virality accelerates, the societal stakes expand from individual relevance to public discourse, safety, and democratic health.

Because attention is finite, recommendation is inherently a form of amplification—deciding who and what gets seen. The chapter argues that understanding this influence requires measuring amplification, yet doing so is hard: “value” is elusive, engagement is an imperfect proxy, popularity can be self-reinforcing, and user–algorithm–society interactions evolve over time. These dynamics can normalize fringe or emotionally charged content and scale real-world consequences, all within markets dominated by a few platforms fortified by network effects and lobbying. The authors propose treating amplification transparency like nutrition labels: standardized, evolving metrics that illuminate how content is selected and ranked. Establishing shared, rigorous definitions is presented as a prerequisite for accountability, effective oversight, and healthier digital ecosystems.

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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