Watching videos on YouTube is quite possibly one of the most interesting parts of most of our days. After a busy day at work or school, coming home and unwinding with a video from your favorite creator can be extremely stress-relieving. YouTube provides a platform for us to find a range of videos and creators, in whatever genre interests you.
Navigating Youtube Recommendations
Now you might have noticed that the videos that are recommended to you are often different from the ones recommended to your near and dear ones. You are probably fascinated by the process of YouTube tailoring its recommendations to fit your requirements.
This extremely personalized feature not only improves your affection for this app but may also pique your interest as to how this process works in the first place. Well, for all the readers who allow your curious side to flourish, this article is the perfect place. Let us now take a peek into the series of steps by which you are able to find the kind of YouTube videos that you like.
The YouTube recommendation system comes into play in two main places, the homepage and the ‘’Up Next’’ option. The homepage consists of the page that you are greeted by when you log into the website or app. It consists of a mix of videos that the algorithm thinks you might enjoy. The ‘’Up Next’’ panel is something you will find on the right side of the video you are currently watching. It is usually followed by a series of videos that are either related to the video you are watching, or to the kind of content that you enjoy on a regular basis.
Signals used by the recommendation system
The YouTube algorithm determines the kind of videos you might like by using a series of features, also known as signals, to provide you with the array of some of your favorite videos. These signals involve important indicators such as survey responses, number of clicks, the watch-time as well as the usage of features like sharing, liking, or even disliking a video. So, what exactly are these features? Surveys are usually conducted after you have watched a video, by asking you to rate the video out of five stars. If you give the video a low rating, then you are usually given a follow-up survey to gather more information on it. These ratings indicate the valued watch time, which means the watch time that was an outcome of your interest in the video.
Click is also quite an important feature of this analysis, although, over time its importance has declined due to the rise in deceptive thumbnails which causes people to click on videos that they might not enjoy. Watch time means the same as the name indicates, the time that you spent watching a certain video. A high watch time is a positive indicator, and this sends signals to the algorithm to recommend more videos in the same genre or from that respective channel. Likes and dislikes also play an important role, as alike would garner more such videos from the algorithm, whereas a dislike would prevent such videos from being shown. Sharing a video also predicts that more such videos should be recommended to the user.
Combination of performance and personalization
With a seemingly perfect combination of performance-based signals such as surveys, likes, dislikes, average percentage viewed, duration of views, and the click-through rate, the videos show up on your homepage. If a higher percentage of people react well to it, then it shows up on the homepages of a larger number of people, depending on whether the genre fits their usual recommendations. That being said, YouTube does not just show you whatever videos a large chunk of the population is watching. It also tailors these videos based on your past history of watching certain kinds of videos. This is where the feature of your watch history plays a prominent role. This algorithm also manages to retain its sensitivity as and when your tastes in videos change over a period of time.
To conclude, YouTube has a number of analytical indicators to figure out what kind of videos it wants to display on your recommendations. If you are a creator, then you can delve deeper into these analytics in order to find out how best to use them to reach a larger audience. If you are just a user, then hopefully, you gained some useful insight as to how you are able to find your beloved YouTube videos so swiftly.