Netflix Inc.

Alexander Christian Pavlakis

Prasanna Vijayanathan

Andrew David Eichacker

The disclosed computer-implemented methods and systems leverage machine learning techniques to generate disruption predictions associated with digital media sessions. For example, the methods and systems discussed herein generate input features for a deep neural network that represent various characteristics associated with a session. By applying the deep neural network to the generated input features, the methods and systems described herein generate an accurate disruption prediction in addition to an attribution report detailing which of the characteristics represented among the input features had the greatest impact on the disruption prediction. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for predicting disruptions in digital content system sessions

The disclosed computer-implemented methods and systems leverage machine learning techniques to generate disruption and delight predictions associated with digital media sessions. For example, the methods and systems discussed herein generate input features for a deep neural network that represent various characteristics associated with a session. By applying the deep neural network to the generated input features, the methods and systems described herein generate accurate disruption and delight predictions in addition to an attribution report detailing which of the characteristics represented among the input features had the greatest impact on the generated predictions. Various other methods, systems, and computer-readable media are also disclosed.

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New York City, NY, United States of America

Alexander Christian Pavlakis