Latent Models (Shallow and Deep) for Recommender Systems
In this talk, we will survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we will discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will discuss techniques for embedding discrete user action events into continuous but latent space for building a context aware collaborative filtering model for personalization and recommendations. Finally, we will highlight promising new directions in this space.
Ko-Jen (Mark) Hsiao is a senior research scientist at Netflix, where he researches and implements innovative algorithms to optimize Netflix's core personalization. He has extensive experiences in applying machine learning at scale, building and A/B testing ranking and recommendation systems. Before he joined Netflix, he was a data scientist at Whisper, an anonymous social network app with over 30 million monthly active users. He developed and implemented several end-to-end machine learning systems used by the company. He obtained his PhD from University of Michigan, where he focused on combining disparate information for machine learning applications.