Research at Netflix is aimed at improving various aspects of our business. Research applications span many areas including our personalization algorithms, content valuation, and streaming optimization. To maximize the impact of our research, we do not centralize research into a separate organization. Instead, we have many teams that pursue research in collaboration with business teams, engineering teams, and other researchers. This allows for close partnerships between researchers and the business or engineering teams in each area. In addition, research that applies to the same methodological area or business area is shared and highlighted in discussion and debate forums to strengthen the work and its impact. These forums also serve to identify and motivate future research directions.

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Recommendation algorithms are at the core of the Netflix product. They provide our members with personalized suggestions to reduce the amount of time and frustration to find something great to watch. Because of the importance of our recommendations, we continually seek to improve them by advancing the state-of-the-art in the field. We do this by using the data about what content our members watch and enjoy along with how they interact with our service to get better at figuring out what the next great movie, TV show, or game for them will be. We go beyond validating our ideas on historical data to understand how people actually respond to changes in our recommendation system by running online A/B tests and measuring long-term satisfaction metrics. These experiments also provide us with new insights to further improve our research and product. This cycle of experimentation has led us to move beyond rating prediction, made famous by the Netflix prize, and into personalized ranking, page generation, search, messaging, and much more.

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Meet Up with Netflix

IN PERSON: Wed. 16 Oct from 11:00 AM - 12:00 PM CEST in Sala Azzurra (Chamber of Commerce< 1st Floor