At OpenTable, we help diners find the best dining experiences, wherever they travel. One of the key problems for accomplishing this is providing personalized recommendations. We have been leveraging our large corpus of unstructured reviews to build models to improve the accuracy of these recommendations. We will discuss how we use Spark both for the training of our recommenders, and for the natural language processing of the reviews to generate topic models.
Pablo Delgado is a Senior Software Engineer, he works on building infrastructure for machine learning for Personalized Recommendation Algorithms at Netflix. Previously he was working on the recommendation systems stack for personal restaurant recommendations at Opentable. Pablo obtained a degree in Mathematics and Computer Science in University College London, London United Kingdom, where he worked on Graph based Methods for Collaborative Filtering.