Google Inc.

Ajay Kumar Bangla

Yi Zhang

Bahman Rabii

Gaofeng Zhao

Niyati Yagnik

Manohar Karkal Prabhu

Michael Eugene Aiello

Timothy S Lipus

Creighton Thomas

Shilpa Arora

Yingwei Cui

Colin Craig McCulloch

Colin McCulloch

Bikash Koley

Emilie Jeanne Anne Danna

Xiaoxue Zhao

Wenjie Jiang

Alireza Ghaffarkhah

Christoph Albrecht

Benjamin Preskill

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

The present disclosure provides a probabilistic framework that can calculate the probability of fulfilling demands for a given set of traffic flows. In some implementations, the probability of fulfilling demands can be based on the probability of infrastructure component failures, shared risk link groups derived from a cross-layer network topology, and traffic engineering (TE) considerations. The consideration of the cross-layer network topology enables the systems and methods described herein to account for the relationship between the physical and logical topologies.

Parallelized network traffic flow availability simulation using stochastic process and traffic engineering algorithms

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San Jose, CA, United States of America

Ajay Kumar Bangla