YouTube has provided a handy new overview of how its video recommendation system works to help creators build an audience on this platform

The primary aim of creators on YouTube is to build a strong audience that gives more views on this platform as there are more than a billion active viewers on this platform. Creators have many talents but they don’t know exactly how the video recommendation system of YouTube works. Keeping in view the requirements of many creators, YouTube has published a handy overview of how its video recommendation system works to help creators get more views. A product manager for discovery at YouTube named Rachel Alves has presented the overview and insight about the recommendation working of videos to help people understand the system that influences how the videos are disseminated.

Alves said that you don’t need to be an expert to understand the algorithm of analytics; even a common person can also get the understanding or working of the algorithm to become a system in the future. Creators should understand about the basic aim of YouTube recommendation is to help viewers to find the videos according to the interest that gives them satisfaction, in this way the viewer will come back if they find a good experience on this platform. YouTube has changed what it optimizes with time. If we take a look back in 2011, the videos were recommended by their clicks and views so creators tried to get the clicks and views by showing fascinating or attractive thumbnails to get people to watch their videos that never give complete satisfaction to a view because most of the videos had a fake thumbnail for clickbait.

Similarly, watch time is important, but the satisfaction of viewers with the time they spend on YouTube is more important. Now creators have to get the information about what the viewers like to watch and raise authoritative content and reduce the speed of borderline violate content. Most of the feedback that YouTube received is people’s home feed was bring filled with sensational or off-putting videos, so it changed the focus to watch time as a key metric instead in 2012.

It is simple to understand that if you are spending more time watching a video on this platform means that you are interested in the quality of the content. However, Alves says that watch time is not a perfect metric either, because when you spend more time watching videos does not mean that you will end up feeling good about that time spend later. Because of this YouTube has been seeking to better define ‘’quality or value of content’’ optimizing more towards the satisfaction of the viewers. To get authentic information from the viewers, YouTube sends millions of surveys to the viewers to get the complete details about what they want to watch on this platform. YouTube does not share this information with the creators because it does not have complete details about each individual clip to provide strong feedback; rather it uses the information to make its algorithm and system better. Now YouTube is adding more satisfied data, YouTube also gets the information when the viewer clicks on the "Not Interested" option in the video menu.

The Home page and suggested listing use a separate algorithm, so the idea that YouTube uses only one algorithm is not correct according to Alves. Creator can’t optimize for a traffic source rather they can only optimize for people or the viewers of the platform. If the creators want to increase the views from the home page, they should try to consider their content from the viewpoint of somebody who is endorsing their videos due to their interest in the clip. Recommended offers viewers a selection of videos they are most likely to watch next, based on their preceding activity. Many creators are using the tactics of video series or episodes that attract people to move to the next video from prior. Alves says that now the algorithm of YouTube works in such a way as "videos for viewers" not "viewers for videos".

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