In this chapter, we discussed the main techniques for building a recommender system. In a user-based scenario, we assume that we have enough pieces of information about the users to be able to cluster them, and we implicitly assume that similar users would like the same products. In this way, it's quick to determine the neighborhood of every new user and to suggest products positively rated by their peers. In a similar way, a content-based scenario is based on the clustering of products according to their peculiar features. In this case, the assumption is weaker, because it's probable that a user who bought an item or rated it positively will do the same with similar products.
Then, we introduced collaborative filtering, which is a technique based on explicit ratings, used to predict all missing values for all users and products. In the memory-based variant...
