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How do online platforms like Netflix, Spotify, and YouTube recommend you new content?
One of the challenges that platforms like Netflix and Spotify face is recommending new content that users will enjoy. Showing the same content over and over again can bore users, and make them less likely to continue using the platform. Randomly recommending new content is not a good idea either, as they could end up recommending content they have no interest in.
Instead, these companies rely on sophisticated mathematical and computer science techniques to tailor recommendations specifically for you. Often, these are based on content you've already liked while using the platform. Sometimes, however, these new recommendations are not at all similar to the content you've already experienced - but you find that you really like them, and discover something new!
This pattern of using your behaviour and data to predict what you will like (before you even know you do) is just one application of the field of data science.
As someone uses Netflix, they often give a rating of "Like" or "Dislike" to the content they watch. How does Netflix use this information to produce recommendations for new content, and increase the chances the user will enjoy it?
In this activity, you will learn some of the mathematical background and techniques used to make this happen, and even make your own recommendations!
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