Recommender Systems are one of the most popular applications of Machine Learning systems. Due to their widespread success, they are quickly becoming ubiquitous to a lot of businesses. Traditionally, collaborative filtering and matrix factorization techniques were used to solve these problems.

In the last couple of years, this trend has been changing. Due to the massive success of effectively training deep neural nets, new approaches have been developed by leveraging the tools and modeling flexibility from the Deep Learning ecosystem.

This hack session gives a primer into these concepts using neural network architectures.