Abstract: 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.
Bio: Oliver Gindele is the head of Machine Learning at Datatonic. He studied Materials Science at ETH Zurich and moved to London to obtain his PhD in computational physics from UCL. Oliver is passionate about using computers models to solve real-world problems for which he joined Datatonic to create bespoke machine learning solutions. Working with clients in retail, finance and telecommunications Oliver applies deep learning techniques to tackle some of the most challenging use cases in these industries.