If movie belongs to a certain genre we put 1, otherwise, we put 0. Note that some TV Shows satisfy only one feature, while others have more. Also, you could use weighted values here as well. For example, you can say that Stranger Things on the sci-fi scale scores 0.7. We use just k-hot encoding for now. The next thing we do is represent the user using the same features by using the ratings she gave. To do this, we scale each feature by the user’s ratings and normalize it. As a result, we get the user features vector. To sum it up, steps for getting user features vector are:

Multiply the movie feature matrix by user’s ratings

Aggregate by summing across each feature dimension

Normalize the result

In our example that would look something like this: