Google Inc.

Ruixue Fan

James Robert Koehler

Matthew Strecker Burriesci

Joseph Kelly

Jon Vaver

Stefan F Schnabl

Yueqing Wang

Harrison Mark Gordon

William Martin Halpin, Jr

Nicolas Remy

Sean Patrick Miller

Antonella Pavese

Xiaojing Wang

Stephanie Sapp

Andrew Smith

Kirsten Paige Yee

Yijia Feng

The present disclosure provides systems and methods for entropy balanced population measurement. Entropy balancing is a statistical technique for preprocessing data to achieve covariate balance. Weighting coefficients may be dynamically adjusted to satisfy balance conditions or constraints to adjust for inequalities in representation, thereby improving covariate moments. Using entropy balancing and linear regression analysis with panel content exposure and results data provides a mechanism to estimate the effects of multiple exposure vectors simultaneously, including instances where panelists are exposed to a vector multiple times. Data may be obfuscated or anonymized for preprocessing via a double-encrypted intersection-based extraction mechanism, allowing both measurement systems and panel providers to retain confidential information.

Systems and methods for entropy balanced population measurement

Systems, methods, and computer-readable storage media for attribution modeling using withheld or near impression data are provided. One method involves determining, for a first content item impression, withheld or near impressions for a competing content item within a content auction. The method further involves identifying a first set of paths including a sequence of events that includes an interaction with the first content item impression. The method further involves identifying a second set of paths, each including the sequence of events with the competing content item impression replacing the first content item impression. The method compares conversion metrics for the first and second paths to determine attribution credit for the first content item impression.

Attribution modeling using withheld or near impressions

Methods and systems of reducing computer resource utilization to update rankings based on dynamically generated content are described. The method includes identifying a ranking of content experience types of a list of content experience types. Each content experience type including parameters to generate content experience documents. The method also includes updating the ranking of content experience types. The updating includes iteratively selecting content experience types from the ranking of content experience types to generate an update subset of the ranking of content experience types, generating, for a client device, a feedback request document including a plurality of content experience documents, and receiving feedback information related to the plurality of content experience documents included in the feedback request document to update the ranking of the content experience types included in the update subset.

Dynamic resampling for ranking viewer experiences

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Great Neck, NY, United States of America

Ruixue Fan