A Tensorflow recommender system for news — Fabricio Vargas Matos (Hearst TV R&D)

News recommendations are particularly challenging given the high number of new contents produced every day and the fast deterioration of its value for the users, demanding models and infrastructure able to deal with those nuances and serve a newly trained model about 100 times per day. Attending this presentation you're going to follow a detailed overview of how R&D team of Hearst's TV division is putting together Google BigQuery, Kubernetes cluster and Tensorflow to build a hybrid recommendation system combining model-based matrix factorization, content recency, and content semantics through NLP.

Fabricio joined the Hearst TV R&D team as a full-time Data Science Consultant in January 2017 to help them to design and implement their next generation recommending system. He have first class BS (1999) and MS (2004) in Computer Science, with a strong math background, working for a decade as a Senior Software Engineer and an Entrepreneur, and since 2016 Fabricio is fully dedicated to Data Science.