The past few years have seen the tremendous success of deep neural networks in a number of complex machine learning tasks such as computer vision, natural language processing and speech recognition. For these reasons, deep learning have been already well stablished in the area, while we now start to see deep neural networks deliver on their potential for improvement in recommendation systems technology. The aim of the tutorial is dual: 1) to briefly introduce deep learning techniques that have been and are used in recommender systems such as recurrent neural networks and convolutional networks 2) to present the current state-of-the-art collaborative filtering and content-based methods that use deep learning techniques to provide recommendations. The tutorial will focus on the deep learning methods that are mostly used in recommender systems, from recurrent networks to convolutional networks and transformer models. We will also touch upon the emerging topic of deep reinforcement learning for recommender systems. The tutorial does not require an in depth prior knowledge in deep learning since there will be an introduction to the relevant techniques, e.g., recurrent neural networks, convolutional networks, word2vec embeddings.
Short Bio
Anoop Deoras is an applied researcher at Netflix where he leads various deep learning projects spanning several products from recommendations to search to home page construction to machine translation for localization. Before joining Netflix, he was working with Microsoft working on Cortana, a virtual personal assistant, innovating and applying advances in spoken language understanding technologies. He did his Ph.D. from Johns Hopkins University where he demonstrated the first ever integration of recurrent neural networks in large vocabulary speech recognition decoders. Anoop is an elect IEEE senior member.