Introduction to Recommendation Systems

Building a Recommendation System is not a trivial task and it comes with its own set of problems and challenges. This article is an effort to provide readers a deeper insight into building recommendation systems. A Recommendation system is an application of machine learning that provides recommendations to users on what they might like based on their historical preferences. It can be further defined as a system that produces individualized recommendations as output or has the effect of guiding the user in a personalized way to interesting objects in a larger space of possible options. Collaborative methods for Recommendation systems are methods that are based solely on the past interactions recorded between users and items in order to produce new recommendations. These interactions are stored in the so-called "user-item interactions matrix".