Imagine an online shop with thousands of articles. If you're not a registered user, you'll probably see a homepage with some highlights, but if you've already bought some items, it would be interesting if the website showed products that you would probably buy, instead of a random selection. This is the purpose of a recommender system, and in this chapter, we're going to discuss the most common techniques to create such a system.
The basic concepts are users, items, and ratings (or an implicit feedback about the products, like the fact of having bought them). Every model must work with known data (like in a supervised scenario), to be able to suggest the most suitable items or to predict the ratings ...
