Geometric Deep Learning in Multigraphs for Movie Recommender Systems
Movie studios face a complex landscape. AI isn’t going to help make movies better, but maybe can help make them more successful. Product recommendation systems are becoming important during the movie greenlight process and as part of machine learning personalization pipelines. Collaborative Filtering (CF) and generative natural language models applied to movie scripts have been shown to increase performance, but tend to underperform for movies that cross genres or are novel. Geometric Deep Learning can increase recommender performance in extreme ‘cold start’ situations, which is the kind of situation a studio faces when deciding whether to greenlight a movie. GDL models are equipped to naturally propagate ‘neighbors’ data to improve the prediction, and to identify/isolate patterns in the training signal that are intrinsic to the local geometry of the multigraph structure. In this talk we’ll discuss our current GDL implementation, how we apply the model at different stages of the production process, how we use the graph spectrum and the movie harmonics to better understand movie positioning before the movie is made, and what related promising areas we are actively exploring.
Miguel Campo is SVP of Data Science & Analytics at Twentieth Century Fox Film Corp. Miguel’s expertise is at the intersection of machine learning and mathematical modeling of customer behavior. At Fox, he heads up the team developing the machine learning pipeline to support all aspects of the film business, from greenlight to theatrical release and home entertainment. Prior to Fox, Miguel led the data science practice at EY Media Advisory, Convertro (now part of Verizon’s Oath), and Disney Science. Miguel is an electronics engineer with a PhD in Information Systems from NYU Stern and a Post-Doc from Dartmouth College. He holds a number of patents in algorithmic modeling, has published in peer review journals, and is actively working with academics in AI and the social sciences to understand key AI governance issues and to articulate feasible industry solutions. He lives in LA with his wife and two daughters, and in his free time enjoys trail running and surfing.