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Recommender systems are widely used by e-commerce and services companies worldwide to provide the most relevant items to their users. Over the past few years, deep learning has demonstrated breakthrough advances in image recognition and natural language processing. Meanwhile, new approaches have been published which apply deep learning techniques to recommender systems, further expanding the use cases of neural networks. Some of these novel systems already display state-of-the-art performance and deliver high-quality recommendations. Compared to traditional models, deep learning solutions can provide a better understanding of user's demands, item's characteristics and the historical interactions between them.

In this talk, Oliver will discuss how some of these novel models can be implemented in the machine learning framework TensorFlow, starting from a collaborative filtering approach and extending that to more complex deep recommender systems.

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