AI in video content personalization: How do algorithms influence our recommendations?

You turn on Netflix after a long day of work. Instead of wading through endless lists of shows and movies, you immediately see suggestions tailored to your preferences. You're scrolling through TikTok, and each video pulls you in more than the last.

This is no accident - it’s the result of advanced artificial intelligence (AI) algorithms that analyze your behavior and customize recommendations specifically for you. Learn more about how it works!

The secrets of recommendation algorithms

Ever wondered how streaming services can so accurately guess your tastes? Behind the scenes, powerful AI mechanisms like collaborative filtering and content-based filtering are at work.

Collaborative filtering analyzes the preferences of similar users. If you and your friend like the same shows, the algorithm assumes you’ll enjoy a movie they recently watched. Meanwhile, content-based systems examine the characteristics of the content itself to provide recommendations. These might include genres, actors, or themes.

But where does AI get all this data? Every action you take on the platform is carefully logged: viewing time, clicks, likes, search history, and even interactions with ads. Algorithms process this information to create a model of your behavior and preferences. The more data they have, the more accurate the suggestions will be.

Personalization algorithms constantly evolve, utilizing increasingly advanced techniques. One of these is deep learning. Neural networks analyze data on multiple levels, allowing them to detect subtle patterns in your preferences. As a result, the recommendations become more accurate, reflecting both obvious and less apparent choices.

Under the Microscope: Netflix, YouTube, TikTok

Let’s take a closer look at how this works on some of the most popular platforms. Netflix is known for its advanced personalization - 75% of content watched by users comes from recommendations. Its system analyzes not only what you watch but also your ratings of the titles you’ve seen. Based on this data, it suggests new shows and movies you might enjoy.

Interestingly, Netflix personalizes not only content but also how it’s presented. Algorithms choose the graphics, descriptions, and even the order of titles on the homepage to best match your preferences. That’s why the layout may look completely different for you compared to your friend, even though you both have access to the same content.

YouTube - watch history and search patterns

YouTube takes personalization further by tracking watch history, search patterns, subscriptions, and likes. This allows suggested videos to be tailored to both your interests and current trends as well as new content from subscribed channels.

But that’s not all - YouTube also uses data about your location and time of day. In the morning, it might suggest short news clips, in the afternoon, entertainment for a work break, and in the evening, longer, relaxing videos.

TikTok - unique for everyone

And what about TikTok, the king of short-form video? Personalization here works at lightning speed. After just a few minutes of scrolling and reacting, the algorithm quickly "learns" what you like. Every tiny detail is analyzed - how long you watch a video, whether you scroll past, and if you react with a like or comment. Based on this, your homepage rapidly fills with content "just for you".

Every TikTok user has their own unique profile of preferences, and the algorithm personalizes content recommendations based on this. That’s why two users can have completely different experiences on the app, even if they started with similar interests.

Does personalization have only advantages?

There’s no denying that personalized content brings many benefits. Above all, it saves time and frustration in searching for materials that interest us. Thanks to accurate recommendations, we quickly find what we’re in the mood for without sifting through less relevant suggestions.

Social media owners know that personalized content keeps you on their platform longer. After all, why look for entertainment elsewhere when everything is handed to you on a silver platter? Of course, platforms benefit from this - longer watch times translate into higher ad and subscription revenues.

Benefits for business and beyond

Personalization is also good news for advertisers. Ads tailored to user interests are almost always more effective than generic messages. Personalized campaigns are more likely to capture attention, generate more clicks and conversions, thereby optimizing marketing spend.

However, the benefits of personalization go beyond business metrics. Well-tailored content can have a real, positive impact on our lives.

Think of an algorithm that, noticing your interest in health, suggests motivational videos about exercise and healthy eating. Or a system that, responding to a dip in your mood, serves uplifting content. Personalized recommendations can inspire, educate, and support us in our daily challenges.

The dark side of algorithms

Of course, there’s another side to this coin. One of the biggest risks of such systems is the so-called filter bubbles. When algorithms show us only what we like and are familiar with, we can get stuck in our comfort zone, cut off from diverse viewpoints and topics. This can lead to a narrowing of horizons and reinforcement of our opinions.

Imagine two people with drastically different political views, each receiving personalized news on Facebook. One sees only content that confirms their worldview, while the other sees only opinions from the opposite spectrum. Instead of dialogue and exchange of ideas, we have two bubbles that never meet. This is how deeper social divides are created.

We discussed this issue more extensively in our article, about AI and the theory of a dead internet.

Disinformation and radicalization

Another problem is the risk of amplifying disinformation and extreme content. AI favors materials that grab attention and generate strong reactions - and often these are fake news or controversial opinions. If the system notices that you frequently click on such content, it will show you more, drawing you into a spiral of disinformation.

YouTube's algorithm, for example, has long been criticized for promoting conspiracy theories and radical content. Watching just one controversial video could suddenly flood you with similar recommendations. While YouTube has taken steps to reduce this, the problem of fake news and radicalization on the internet persists.

Privacy under scrutiny

Privacy is another important concern. Personalization relies on gathering vast amounts of data about users - their behaviors, preferences, and even moods. Although platforms assure us they protect this information, there’s always a risk of leaks or misuse.

The infamous Cambridge Analytica scandal showed just how valuable and potentially dangerous this data can be. The personal data of millions of Facebook users was used to profile voters and influence their decisions. This raises serious concerns about privacy in the era of Big Data.

Moving towards responsible personalization

So how can we balance the advantages of personalization with its challenges? The future lies in the development of increasingly sophisticated AI algorithms that can more accurately tailor content while ensuring diversity and user safety.

Contextual recommendations

An exciting direction is contextual recommendations that take into account the user’s current mood and needs. Such a system might offer an energetic playlist for exercise in the morning, relaxing music for work in the afternoon, and soothing tunes for sleep at night - all based on analyzing your state of mind.

The development of generative AI and machine learning (ML) opens new possibilities for video content personalization. In the future, algorithms may not only select existing content but also create personalized materials from scratch.

Balance and transparency

Social platforms face the challenge of providing personalized, engaging content while maintaining diversity and avoiding filter bubbles. It’s important that algorithms remain neutral and fair, not favoring certain materials at the expense of others.

YouTube’s "Featured Videos" section is a good example. Regardless of your interests, occasionally you’ll see a valuable piece on current topics among the recommended videos. This helps break out of your bubble and expand your horizons.

Platforms should also educate users about how algorithms work and how they affect what we see. It’s about giving greater control over privacy and personalization settings, as well as the ability to report inappropriate recommendations. Transparency and choice are the foundation of trust between platforms and users.

Be a conscious consumer

Finally, it’s important to emphasize that as users, we also have an impact on how algorithms perceive us. We should be aware that every action we take on a platform - every click, like, or watched video - sends a signal to AI, which shapes our future recommendations.

Actively shaping your experience

That’s why awareness and a critical approach to the content we are served are so important. Don’t be afraid to step outside your bubble, explore new topics, and engage with different perspectives. The more diverse your interactions with the platform, the more balanced and interesting content it will offer you.

It’s also worth actively using privacy and preference management tools on platforms. Most services offer some control over what data they collect and how they use it for personalization. Take a moment to review these settings and adjust them to your needs.

The future is in our hands

Video content personalization is a fascinating example of how artificial intelligence is changing our everyday media consumption experiences. While it brings many benefits, it’s not without its challenges and controversies.

It’s important for us as viewers to stay vigilant. Check how algorithms select videos for you, and make sure to watch a variety of content, not just what AI suggests.

In the future, personalization will continue to evolve. New ideas will emerge, but we’ll need to use them wisely. Seek balance - use the system’s suggestions, but don’t forget about other interesting materials. Only in this way will AI truly serve you well!