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.
Anoop Deoras is a Lead Researcher at Netflix, where he leads the algorithmic innovation and productization of deep learning based recommender system models. He is interested in building the next generation of Machine Learning algorithms to drive the Netflix experience. Before that, he was a Lead Researcher at Microsoft, working on Cortana, an AI based virtual personal assistant for Windows OS. He holds a PhD from Johns Hopkins University where he proposed innovative algorithms for the first ever successful integration of Recurrent Neural Network based language models in Large Vocabulary Continuous Speech Recognition and Statistical Machine Translation.