Google Inc.

Shilpa Arora

Yi Zhang

Bahman Rabii

Gaofeng Zhao

Niyati Yagnik

Manohar Karkal Prabhu

Timothy S Lipus

Michael Eugene Aiello

Creighton Thomas

Ajay Kumar Bangla

Yingwei Cui

Colin Craig McCulloch

Colin McCulloch

Yingweii Cui

A method and system for adjusting ads auction using predicted user responses to an in-ad survey is provided. The method includes (1) providing a content item associated with an actionable object, which when selected, causes a computing device to present a plurality of interactive elements each corresponding to a different one of a plurality of reasons for restricting the content item; (2) receiving, from the computing device, data indicating a particular reason, of the plurality of reasons, for restricting the content item, and the particular reason corresponding to a particular interactive element, of the plurality of interactive elements, that was selected by the user; and (3) updating, using the received data, a content selection model for selecting content items, wherein the content selection model is associated with the user.

Adjust ads auction using predicted user response to an in-ad survey

The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Systems and methods for selecting third party content based on feedback

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Sunnyvale, CA, United States of America

Shilpa Arora