Large-Scale Deep Learning for Content-Based Recommendation Systems

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Deep Learning algorithms have been proposed in recent years to move machine learning systems towards the discovery of multiple levels of representation. They have had important empirical successes in recent years in a number of traditional AI applications such as computer vision and natural language processing. This proposal focuses on the challenges involved in extending their reach to the broad application area of user modeling, with a particular focus on recommendation systems. Recommendation systems have become important in online user interfaces, data-driven marketing, computational advertising and video games. The challenges targeted here regard the scale and efficiency of deep learning algorithms and the opportunity of extending recommendation systems to the more general task of personalized content generation.