Nick Pentreath, a Principal Engineer at IBM, explains how in the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. This talks explores recent advances in this area in both research and practice. I will explain how deep learning can be applied to recommendation settings, architectures for handling contextual data, side information, and time-based models, and compare deep learning approaches to other cutting-edge contextual recommendation models, and finally explore scalability issues and model serving challenges.




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