YouTube is the number one video sharing and streaming platform that has 2 billion active subscribers and over 500 hours of video uploaded every day, so there is always something to watch and learn.
YouTube was created in 2005 by former PayPal employees Steve Chen, Jawed Karim, and Chad Hurley, who at first decided to create a dating site that allowed users to share videos of themselves and get matched, which was not a success, and instead people started posting random videos of themselves, which started getting engagement and gave birth to the idea of YouTube.
Have you ever wondered how almost every time you watch a video on YouTube, the next one is always something related or something you like?
Here is how it works
YouTube has a recommended system that uses machine learning to recommend related videos to users. These videos are recommended based on what you were watching before, related topics, and your watching history.
Suggested videos can be from any channel, especially those you have subscribed to. The algorithm tries to predict what you will be interested in in the future so that you can keep watching because the platform is dependent on your watch hours.
The YouTube Recommender System uses a deep learning technique known as candidate generation to select the top 100 videos that users will like out of the billions of videos that have been uploaded on the platform. Then it uses the ranking phase technique to assign scores to videos based on user query input, previous videos, and video duration.
Apart from these techniques we have discussed, YouTube also has a lot of other deep learning techniques it uses to keep its users engaged, and this is what has made it one of the best video streaming platforms over almost two decades.
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Image Source – Youtube
