For many years, the main goal of the Netflix recommendation system has been to get the right titles in front of each member at the right time. For instance, the 2006 Netflix Challenge helped spur new research in low-rank matrix decomposition and collaborative filtering. Today, we use nonlinear, probabilistic, and deep learning approaches to make even better rankings of our movies and TV shows for each user. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why this is a good title for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. Our image personalization engine is driven by online learning and contextual bandits to reliably handle over 20 million personalized image requests per second. Finally, while machine learning is great at learning to make accurate predictions, predictions must be made in order to take actions in the real world. Currently, we are working on integrating causality and fairness into many of Netflix’s machine learning and personalization systems.
Tony Jebara is director of machine learning at Netflix and professor on leave from Columbia University. He has published over 100 peer-reviewed papers in leading conferences and journals across machine learning, computer vision, social networks and recommendation. His work has been recognized with best paper awards from the International Conference on Machine Learning and from the Pattern Recognition Society. He is the author of the book Machine Learning: Discriminative and Generative. Jebara is the recipient of the Career award from the National Science Foundation as well as faculty awards from Google, Yahoo and IBM. He has co-founded and advised multiple startup companies in the domain of artificial intelligence. Jebara has served as general chair and program chair for the International Conference on Machine Learning. He obtained a PhD from MIT in 2002.
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