The role of algorithms in personalizing the digital experience: how do they work and how do they affect our preferences?

The role of algorithms in personalizing the digital experience and what has changed since 2023.

When this article was first published (March 2023), recommendation algorithms were already ubiquitous—but they primarily operated based on click history and browsing behavior. By 2026, the landscape is different: large-scale language models (LLMs) have become integrated into personalization systems themselves, and platforms like Google, Meta, and TikTok have rewritten their recommendation architectures around generative AI.

The result? Personalization became more precise, faster, and, at the same time, more opaque. This article updates the fundamental concepts and adds what was left out in the original version: types of algorithms, filter bubbles, algorithmic bias, and what the LGPD (Brazilian General Data Protection Law) requires of companies that use these systems.

How personalization algorithms work

The word "algorithm" is used generically, but there are at least three main architectures that operate together on major platforms:

“In 2025, Amazon announced that 35% of its sales originate directly from algorithmic recommendations. On Spotify, more than 30% of streams come from automatically generated playlists.”

What algorithms know about you

Modern personalization systems collect and process much more than just click history. The variables monitored include:

• Attention span: How much time you spent on each piece of content, where you paused, and where you skipped.
• Consumption sequence: The order in which you consume items reveals patterns of mood and context (morning × night, weekday × weekend).
• Implicit interactions: Scrolling without clicking, hovering, reading time — signals you don't know you're sending.
• Contextual data: Location, device, time, and even typing speed are used by some platforms.
• Social graphs: Who you interact with, who you follow, who follows you.

Filter bubbles and echo chambers

The researcher Eli Pariser coined the term. filter bubble (filter bubble) as early as 2011, but the phenomenon intensified dramatically with the maturation of algorithms. The logic is simple: by optimizing for engagement, algorithms tend to show more of what already pleases us—and progressively less of what challenges or contradicts us.

The result has two dimensions:

• Informational echo chamber: You end up consuming news and opinions that reinforce your existing beliefs, without exposure to divergent perspectives.
• Narrowing of tastes: In music, film, and consumer behavior, the algorithm can "freeze" your preferences around a past profile, making genuine discovery difficult.

Warning: the problem of optimization for engagementResearch from MIT and Yale University has documented that content that generates outrage and polarization tends to have up to 70% higher engagement than neutral content. Algorithms that optimize for "time on platform" end up systematically favoring emotionally charged content—regardless of its veracity.

Algorithmic bias: when data discriminates

Algorithms are not neutral. They are trained on historical data that carries societal biases—and, in many cases, amplifies them. Some documented examples:

• Credit algorithms that penalize residents of certain postal codes, replicating historical patterns of geographic discrimination.
• Recruitment tools that disadvantage women's resumes in historically male-dominated fields, due to past hiring practices.
• Social media feeds that deliver lower-quality health content to low-income users, as these profiles generate less advertising engagement.

In the context of digital marketing, algorithmic bias can affect who sees your ads—even if you haven't intentionally targeted by race, gender, or income. Meta, for example, has already been sued in the US for discriminatory distribution of housing ads.

LGPD and personalization: what the law requires

The General Data Protection Law (Law 13.709/2018) has direct implications for any company that uses personalization algorithms in Brazil. The most relevant points are:

The impact of shared login

When multiple users share an account, the algorithms receive contradictory signals and build a profile that does not accurately represent any user. In practice:

• Recommendations become generic or random, losing the usefulness of personalization.
• Advertising data becomes inaccurate, increasing the cost per click for advertisers.
• The data-driven business model — which underpins free access to many platforms — is being subverted.

Not coincidentally, Netflix, Disney+, and other streaming platforms intensified their fight against password sharing between 2023 and 2025, with significant financial results: Netflix recorded an increase of 5,9 million subscribers in the quarter following the implementation of the policy in Brazil.

For marketing professionals: how to use this to your advantage

Understanding the logic of algorithms is a real competitive advantage. Some practical implications:

Central principleAlgorithms optimize for the behavior you reward with attention. If your content holds people's attention for 30 seconds, the algorithm delivers more of your content to those who stayed for 30 seconds. Create content for the behavior you want to generate, not just for reach metrics.

• Niche content performs better than broad content.Modern algorithms are very good at identifying and grouping specific audiences.
• The first window of engagement is critical.— On Instagram and TikTok, the first 15–30 minutes after posting determine the total organic reach.
• Implicit signals are just as important as explicit ones.— Saves, watch time, and replays have more algorithmic weight than likes on most platforms in 2026.
• Frequency consistency matters.Feed algorithms learn your posting rhythm and penalize irregularity with a drop in distribution.
• First-party data is the new gold.With the end of third-party cookies now consolidated, your CRM and email list are the most valuable data assets you possess.