# Learning RecSys through Papers- Implementing a Candidate Generation Model

Sometimes I’m asked how to start learning about recommender systems, and there’s a few go-to papers I always mention; however, without a proper map, it could be alittle difficult for the uninitiated. So, to try to make a gentle introduction, I will walk through an implementation of the candidate generation model based on Deep Neural Networks for YouTube, which I will sometimes refer to as the “Deep Nets” paper. This paper (and its talk) is jam-packed with all sorts of interesting practical recommendation system knowledge and is the perfect starting place for people hoping to understand large-scale recommendation systems. I will implement the key components of the candidate generation model and train the model on the MovieLens dataset in PyTorch, a typical benchmark dataset in the RecSys space. There will also be a few modern flourishes and comments on new developments as applicable. I’ll conclude with outlining a natural extension to this approach to predict multiple outcomes.