We can see that the top feature for both users is Comedy, which means they like simmilar stuff. What have we done here? Well, we not only described items in terms of the mentioned genres, but we have done the same for each user with the same terms. The meaning for a User1, for example, is that she likes Comedy 0.5 but he likes Action 0.1. Note that if we multiply users embeding matrix with the transpose item embeding matrix we will recreate the user-item interaction matrix. Now, this works well for simple examples with few users and items. However, as more items and users are added to the sytem it becomes unscalable. Also, how can we be so sure that the features that we picked are the relevant ones? What if there are some latent features, that we are unable to reckognize. So how can we pick correct features then? This brings us to the matrix factorization.