Hi there, this is the first article of a series called “Hello, <algorithm>.” in which we will give some insights about how different Machine Learning and Artificial Intelligente algorithms work. Today we are going to talk about Gradient Descent, a simple but yet powerful optimization algorithm for finding the local minimum of a function. It is worth to mention that if the function is convex, the value encountered by Gradient Descent will be the global optimum.

A recommender system predicts the likelihood that a user would prefer an item. Based on previous user interaction with the data source that the systems takes the information from (besides the data from other users, or historical trends), the system is capable to recommend an item to a user. Think about the fact that Amazon recommends you books that they think you could like; Amazon might be making effective use of a recommender system behind the curtains. This simple definition, allows us to think in a diverse set of applications where recommender systems might be useful. Applications such as documents, movies, music, romantic partners, or who to follow on Twitter, are pervasive and widely known in the world of Information Retrieval.