How YouTube Recommendation System Has Evolved and Improved

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Vidsaga has come up with a hack video to help marketers know how the YouTube recommendation system has evolved and improved.

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Apart from The Usual Key Elements – Clicks, Watch time, Sharing, Likes, and Dislikes

Youtube uses other methods to refine its recommendation systems:

1. Content Recommendation:

YouTube recommends the content of other people with similar viewing profiles.

For ex-

“if you like tennis videos and Youtube  notices that others who like the same tennis videos as you also enjoy jazz videos, you may be recommended jazz videos, even if you’ve never watched a single one before.”

2. Content Restrictions:

YouTube also bans content that includes false health claims and political misinformation.

3. Human Evaluators:

YouTube uses human evaluators to assess the quality of the information in each channel or video.

Conclusion:

The system utilizes explicit and implicit signals to highlight what each person wants to view while filtering out the worst kinds of content, in order to limit potential harm.

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