Google Inc.

Sen Li

Yang Jiao

Yang Zhang

Xinlong Bao

Amy Richardson

Mert Dikmen

Junji Takagi

Dinah Shender

Ruoyi Jiang

Zhuo Zhang

Jing Wang

Ali Nasiri Amini

Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.

Contribution incrementality machine learning models

Growing community of inventors

Mountain View, CA, United States of America

Sen Li