Netflix is a registered trademark of Netflix, Inc. This website is not affiliated with or endorsed by Netflix.
This is an opinion piece based on our experience with building personalization solutions for media companies. We created a small focus group of Netflix users to help us support our findings.
Netflix is often referred to as the pinnacle of doing it right when it comes to personalization and recommendations. Of course, it does things slightly better than some of the other streamers, but we think there is still room for improvement. Research Gate claims that 80% of Netflix users’ activity is shaped by the algorithm’s personalized recommendations, showing the true impact that personalization can have. However, users still get frustrated and don’t feel that Netflix truly reflects their actual preferences. Netflix is very good at a few things: keeping you on the platform, promoting its own content and making the experience feel personalized without it necessarily being personalized. Those are not the same thing. This blog is a deep analysis based on our expertise and opinion of Netflix’s recommendation algorithms and user experience.
The Paradox of Choice
The Gracenote State of Play report 2025 found that streaming viewers now spend an average of 14 minutes searching for something to watch. There is a vast amount of content out there. On Netflix, there are over 8,000 titles alone. With so much content, platforms have to make sure they are making content discovery as easy as possible. If not, viewers are turning off.
Netflix, although it performs better than competitors, it’s not without its frustrations. The Continue watching rail moves around the homepage, it never seems to be in the same place, meaning that it’s not always seamless to carry on watching your favorites. Sometimes it feels like nothing is in the right place. You can spend an hour browsing Netflix’s home screen and you start to notice something odd. The “We Think You’ll Love These” rail, supposedly the most personalized section on the page, is nowhere near the top. You scroll past promoted originals, trending content, “New Releases,” and several genre rows before you get to anything that’s actually about your preferences. By that point, a lot of people have already made a decision or given up.
Stuck in an Editorial Push?
Do you ever think that the platform is built around what Netflix wants you to watch? Original content surfaces aggressively, regardless of your taste profile. If you’ve never shown any interest in a particular genre and Netflix has just dropped a big-budget series in that genre, don’t be surprised when it appears front and center.
As Netflix is now a production company, their recommendations feel biased, pushing their own content first rather than content that actually fits the preferences of its consumers. A 2025 survey by TheStreet noted that subscribers frequently “slam” the service for a “bloated pool of low-budget garbage” (Netflix Originals) that pushes out higher-quality licensed content. Those two roles are always quietly fighting each other, and the algorithm is often the tiebreaker, in Netflix’s favor.
The Need to Go Further
The row titles themselves don’t always help. “Because You Watched” sounds more useful than it is. Users can often feel that one action holds too much weight, as soon as you watch one type of asset, whether it’s a certain language, genre or actor, every row tilts that way. It’s an overcorrection that feels less like a recommendation and more like a category tag applied to your whole identity. One choice shouldn’t rewrite everything.
The rows themselves are also fairly static in what they offer. After a couple of months of regular use, patterns emerge. The same films keep cycling back. The same types of shows. There’s a staleness that creeps in, and it’s hard to tell whether that’s a content library problem or an algorithm problem. Probably both.
Netflix has its own rating system. Star ratings based on how well a title matches your predicted preferences. What it doesn’t show you is any external quality signal, no IMDb scores, no Rotten Tomatoes, nothing that exists outside Netflix’s own data. This matters more than it might seem. Netflix’s personalization score tells you how likely you are to watch something. However, because of the no rating system apart from their own, there is no true (independent) rating, so how do you know whether it’s actually good for you? Those are different scenarios. A film might be closely matched to your profile and still be mediocre. Without an independent quality indicator, you’re navigating the catalogue with one eye closed.
Seeing Double?
Compared to their competitors, there is less content repetition in their UI, but some the recommendations are still repeated. Even after watching a show, you will still get it recommended to you.
If you have a developed viewing history, a real profile, built up over months, the system leans further and further into what it already knows about you. The more it knows, the narrower it gets. This isn’t a small thing. Serendipity is what keeps a catalogue feeling alive. It’s how people discover films they’d never have searched for. Without it, a well-established Netflix profile slowly becomes a mirror, reflecting the same tastes back at you, month after month, with diminishing returns. Within the recommendation algorithm, you might not actually discover anything new, which can cause viewers to churn and leave for a different service that can better serve their entertainment needs.
Do Better than Netflix
With a real-time personalization solution like XroadMedia’s, you can go further than the standard recommendations. Our flexible solution allows your teams to be able to build an engaging user experience that delivers results. Check out how we can solve some of Netflix’s problems:
| Netflix Problem | XroadMedia Solution |
Inconsistent rows and repetition of items | Pin rows, build your homepage to fit your business and editorial rules and goals. Tailor rows and whole pages to individuals based on their taste, profile and viewing behavior. |
Boring, meaningless row titles | Natural language AI row titles create engaging, emotive row titles that are not only more exciting on the page, but also connect the catalog with the user’s interests and tell your viewer why they should watch those items. |
| Editorial bias | To have a strong editorial voice is vital, but you can mix it with the preferences of your audience. Boosting content you want your users to watch is a business driver, that delivers more results when it is aligned with what your viewers like. Personalize editorially curated rows, create personalized rows based on rules defined by editorial teams or help your editorial teams with automation in the curation process, including influences outside your service (such as social platforms). |
| Weighting assets and stale recommendations | Our solution doesn’t just take play events into account for recommendations, but all watching behavior. Our unique two-step approach of filtering and content scoring leads to 6x faster learning compared to standard algorithms, meaning your users will always find something to watch. |
Are you looking to improve your personalized experiences and to power an engaging user experience that keeps your viewers returning?
