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A TenserFlow recommender system for news
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| por Fabricio Vargas Matos Seguir 0 Seguidores em 13 nov 2017 |
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29:27

Resumo
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.

Minibiografia

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, working for a decade as a Senior Software Engineer and an Entrepreneur, and since 2016 Fabricio is fully dedicated to Data Science.

Nos dias 21 e 22 de Junho de 2017, São Paulo recebeu a primeira edição do PAPIs.io Connect no Brasil. Com o intuito de apresentar casos reais de utilização de Machine Learning, o PAPIs contou com mais de 20 palestras apresentando cases e ferramentas de empresas nacionais e internacionais.

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