The different recommendation paradigms

In a recommendation task, you have a set of users interacting with a set of items and your job is to figure out which items are suitable for which users. You may know a thing or two about each user: where they live, how much they earn, whether they are logged in via their phone or their tablet, and more. Similarly, for an item—say, a movie—you know its genre, its production year, and how many Academy Awards it has won. Clearly, this looks like a classification problem. You can combine the user features with the item features and build a classifier for each user-item pair, and then try to predict whether the user will like the item or not. This approach is known as content-based filtering. As its name suggests, it is as good as the content or the features extracted from each user and each item. In practice, you may only know basic information about each user. A user's location or gender may reveal enough about their tastes...