Today we’re joined by JJ Espinoza, former Director of Data Science at 20th Century Fox.
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In this talk we start out with a discussion on JJ’s transition from econometrician to data scientist, and then dig into his and his team’s experience building and deploying a content recommendation system from the ground up. In our conversation, we explore the design of a couple of key components of their system, the first of which processes movie scripts to make recommendations about which movies the studio should make, and the second processes trailers to determine which should be recommended to users. We discuss the challenges they’ve encountered fielding these systems, some of the tools that were used along the way, and a few of the upcoming projects that could be layered on top of the platform they’ve built.
Mentioned in the Interview
- Blog – How 20th Century Fox uses ML to predict a movie audience
- Youtube 8m – Github
- Apache Airflow
- Google Data Studio
- Paper: Convolutional Collaborative Filter Network for Video Based Recommendation Systems
- Hal Varian – Artificial Intelligence, Economics, and Industrial Organization
- Check out all of our great series from 2018 at the TWiML Presents: Series page!
- TWiML Online Meetup
- Register for the TWiML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0