Another dot in the blogosphere?

CrashCourse AI episode 15

Posted on: November 24, 2019

It took a while, but CrashCourse finally provided some insights into how YouTube, Netflix, and Amazon make recommendations.


Video source

Long story short: The AI recommendations are based on supervised and unsupervised learning. The interesting details are that the algorithms may be content-based, social-based, or personalised.

Content-based algorithms examine what is in, say, YouTube videos. Social-based algorithms focus on what the audience does (e.g., likes, views, time spent watching). As we have different preferences, algorithms can learn what we like and serve us similar content or content from the same provider.

The recommendations we see on YouTube are a combination of all three and the process is called collaborative filtering. This relies on unsupervised learning to predict what we might like based on what other users similar to us also like/watch.

The AI might make mistakes in the recommendations. This can be due to sparse data (e.g., low views, low likes), cold starts (i.e., AI does not know enough about us initially), and statistics (i.e., what is likely is not the same as what is contextually relevant). A good example of this sort of mistake is online ads.

Some pragmatics: To get good recommendations, we might subscribe and like videos from content creators we appreciate. To avoid getting tracked, we might use the incognito mode in most modern web browsers.

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