YouTube shares how AI, audience satisfaction, context, and multilingual features are transforming its recommendation system for 2025.
In a recent video interview, YouTube Liaison René Ritchie conversed with Todd Beaupré, YouTube’s Senior Director of Growth & Discovery, about how the platform’s recommendation system operates and what creators can anticipate this year.
Their conversation highlighted how factors like the time of day, the type of device used, viewer satisfaction, and the emergence of large language models (LLMs) are influencing YouTube’s algorithms.
Here’s what you should understand about YouTube’s recommendation system and its functionality.
One of the central themes of the interview is YouTube’s focus on matching content to individual viewer preferences.
According to Beaupré:
“Often times creators will say hey, uh the recommendation system is pushing out my video to people or why isn’t it pushing out my video yes they they may ask that and the way the work it works is it… isn’t so much about pushing it out as much as it’s pulling…”
He goes on to explain that YouTube’s home feed prioritizes content based on what each viewer is most likely to enjoy at any given moment:
Metrics & Satisfaction
Although click-through rate (CTR) and watch time are still crucial, YouTube’s system also considers user satisfaction obtained from direct surveys and various feedback indicators.
Beaupré notes:
“We introduced this concept of satisfaction… we’re trying to understand not just about the viewer’s behavior and what they do, but how do they feel about the time they’re spending.”
He explains YouTube’s goal is to cultivate long-term viewer satisfaction:
“…we look at things like likes, dislikes, these survey responses… we have a variety of different signals to get at this satisfaction… we want to build a relationship with our audience just as creators want to do with their fans.”
Evergreen & Trending Content
YouTube’s algorithms can identify older videos that become relevant again due to trending topics, viral moments, or nostalgic interests.
Beaupré cites the system’s ability to pivot:
“…maybe like right now there’s a video that that reaches a certain audience but then like in six months… that makes this video relevant again… if it’s relevant and maybe to a different audience than enjoyed it the first time.”
Context: Time, Device, & Viewer Habits
Beaupré explained that YouTube’s system might display different types of content based on whether a viewer is watching in the morning or at night, and whether they are using a mobile phone or a TV.
“The recommendation system uses time of day and device… as some of the signals that we learn from to understand if there’s different content that is appealing in those different contexts… if you tend to have a preference for watching news in the morning and comedy at night… we’ll try to learn from other viewers like you if they have that pattern.”
Fluctuations In Views
Creators frequently stress about a decrease in their views, but Beaupré points out that this can be a normal rise and fall.
“…the first thing is that that is natural… it’s not particularly reasonable to expect that you’re going to always be at your highest level of views from all time… I would encourage you not to worry about it too much…”
He also recommends comparing metrics over longer periods and leveraging tools like Google Trends:
“…we do see seasonality can play a role… encourage you to look beyond… 90 days or more to kind of see the full context.”
Multi-Language Audio
Numerous creators are looking into multilingual audio to expand their audiences. Beaupré emphasizes that YouTube has adjusted to facilitate dubbed tracks.
“…we needed to add some new capabilities… aware that this video actually is available in multiple languages… so if you’re a Creator who’s interested in extending your reach through dubs… make sure that your titles and descriptions… are also uploaded [in] translated titles and descriptions…”
He also emphasizes consistency:
“We’ve seen in particular creators who dub at least 80% of the… watch time… tend to have more success than those who dub less…”
LLM Integration
In the future, large language models (LLMs) will help YouTube gain a deeper understanding of video content and viewer preferences.
Beaupré says:
“…we’ve applied the large language model technology to recommendations at YouTube to… make them more relevant to viewers… rather than just memorizing that this video tends to be good with this type of viewer… it might actually be able to understand the ingredients of the dish better and maybe some more elements of the video style…”
Beaupré likens it to an expert chef who can adapt recipes:
“…we want to be more like the expert chef and less like the… memorized recipe.”
Key Takeaways For Creators
- The recommendation system focuses on “pulling” content tailored to each viewer rather than pushing videos to everyone.
- While metrics like click-through rate (CTR) and watch time are important, viewer satisfaction (likes, dislikes, and feedback) is also crucial.
- YouTube can bring back older videos if there’s renewed interest in them.
- The time of day and the device being used can affect recommendations.
- It’s normal for view counts to fluctuate—factors like seasonality, trending events, and other external influences can play a role.
- Dubbing and translated titles can help reach new audiences, especially if a significant portion of your content is in the same language.
- Large language models provide a more nuanced understanding, so creators should pay attention to how this affects content discovery.
YouTube plans to share more updates later this year.
