The following is Wikipedia's definition of recommender systems:
"Recommender systems are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item."
Recommender systems have gained immense popularity in recent years. Amazon uses them to recommend books, Netflix for movies, and Google News to recommend news stories. As the proof is in the pudding, here are some examples of the impact recommendations can have (source: Celma, Lamere, 2008):
Two-thirds of the movies watched on Netflix are recommended
38 percent of the news clicks on Google News are recommended
35 percent of the sales at Amazon sales are the result of recommendations
As we have seen in the previous chapters, features and feature selection play a major role in the efficacy of machine learning algorithms. Recommender engine algorithms discover these features, called latent features, automatically. In short, there are latent features responsible for a user...