How recommendations are personalized on YouTube
Posted: Sat Jan 18, 2025 6:26 am
Today, YouTube’s recommendation system is constantly evolving, learning daily by analyzing 80 billion parameters or, as Google calls them, “signals .” These signals complement each other and form the different aspects that are taken into consideration when personalizing recommendations, indicating users’ tastes.
The main signals that YouTube takes into account are clicks, viewing time, survey responses, sharing, likes and dislikes, although none of them, individually, is decisive when recommending a video. The belarus business email database platform complements the information from all of them to make the most appropriate decision.
Clicks
The click is the most basic aspect of engagement, as it implies that the user is interested in the video content. However, as Google reminds us, “Clicking on a video does not mean that the user has actually watched it. This is why, in 2012, we started taking into account watch time.”
The main signals that YouTube takes into account are clicks, viewing time, survey responses, sharing, likes and dislikes, although none of them, individually, is decisive when recommending a video. The belarus business email database platform complements the information from all of them to make the most appropriate decision.
Clicks
The click is the most basic aspect of engagement, as it implies that the user is interested in the video content. However, as Google reminds us, “Clicking on a video does not mean that the user has actually watched it. This is why, in 2012, we started taking into account watch time.”