In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods to address these challenges. We will conclude with a note on future directions we plan on pursuing at Netflix.
Aish is a Director of Machine Learning at Netflix. His org is responsible for the core recommendation and search algorithms used at Netflix. Aish has over 23 years of experience at the intersection of mathematics and software engineering. Prior to Netflix, Aish lead the data science teams at Opentable, Foodspotting, iVistra, and founded the company, vWork, solving large-scale optimization problems.
Sudeep is a Machine Learning Area Lead at Netflix, where his main focus is on developing the next generation of machine learning algorithms to drive the personalization, discovery and search experience in the product. Apart from algorithmic work, he also takes a keen interest in data visualizations. Sudeep has had more than fifteen years of experience in machine learning applied to both large scale scientific problems, as well as in the industry. He holds a PhD in Astrophysics from Princeton University.