1 Recommender systems in action

The chapter introduces how recommender systems quietly shape what we see, buy, and believe across the modern internet. It distinguishes general algorithms from recommender systems that rank items toward specific goals, noting that today’s machine-learning approaches behave like opaque black boxes. Central to their impact is a tight feedback loop: people’s clicks, likes, and dwell time become signals that retrain the system and alter what they are shown next. This mutual influence can steer users toward more extreme content without their noticing, complicating questions of responsibility and making it hard to isolate human choices from algorithmic design.

Recommender systems pervade e-commerce, streaming, news, dating, and especially social media, easing information overload while concentrating attention on a few winners. Social feeds evolved from chronological lists to fully algorithmic timelines, progressing from simple ranking formulas to engagement-optimized machine learning and out-of-network suggestions. The result grows time-on-platform and advertising value, but also brings filter bubbles, cultural homogenization, addiction via intermittent rewards, and creator dependence on opaque ranking signals. Social media can empower activism and real-time reporting, yet also accelerates mis- and disinformation and radicalization, with vulnerable users at particular risk and severe offline consequences when harmful content spreads unchecked. Network effects concentrate power in a small set of for‑profit platforms that shape public discourse while discouraging meaningful oversight.

The chapter frames “algorithmic amplification” as the added exposure content gains because an algorithm selected and ranked it, and argues that measuring it is essential yet difficult. There is no agreed metric of user value; engagement is an imperfect proxy, and popularity can reinforce itself. True impact emerges at the population level through intertwined user–algorithm feedbacks, social interactions, virality, and constantly changing models, trends that can favor emotionally charged content over time. A pragmatic response is transparent amplification reporting—akin to nutrition labels—that covers both curation and ranking and evolves with the systems. Establishing shared, scientifically grounded measures is key so that society, not platforms alone, defines what counts and how amplification should be judged.

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.

FAQ