Google Inc.

Timothy S Lipus

Yi Zhang

Bahman Rabii

Gaofeng Zhao

Niyati Yagnik

Manohar Karkal Prabhu

Michael Eugene Aiello

Creighton Thomas

Ajay Kumar Bangla

Shilpa Arora

Yingwei Cui

Matthew Strecker Burriesci

Colin Craig McCulloch

Colin McCulloch

Christopher Kenneth Harris

Randolph Preston McAfee

Patrick Hummel

Yingweii Cui

Joshua V Dillon

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.

This document describes computer-implemented systems, methods, devices, and other techniques for reducing the amount of redundant third-party content that is transported over a network for presentation with an electronic document. In some implementations, a computing system includes a deduplication apparatus that determines, in response to identifying that a particular instance of third-party content has previously been selected for display in a particular presentation of the electronic document, and based at least on a display characteristic of the electronic document, whether one or more other instances of third-party content that are deemed redundant to the particular instance of third-party content are eligible for selection responsive to the request.

Reducing transmissions of redundant third-party content over a network

Reducing transmissions of redundant third-party content over a new network

Systems and methods for selecting third party content based on feedback

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for ranking content items. In one aspect, a method includes identifying, for a content item, a bid value specifying an amount a content item provider is willing to pay for user interaction with the content item. A predicted performance measure is identified for the content item. The predicted performance measure is adjusted based on a weighting factor for the content item. The weighting factor for the content item is indicative of confidence that the predicted performance measure will match an actual performance measure for the content item and can be different than a weighting factor for another content item identified for inclusion in a ranking with the content item. A rank score is determined for the content item using the bid value and adjusted predicted performance measure. The content item is provided based on the rank score.

Ranking content items using predicted performance

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Sunnyvale, CA, United States of America

Timothy S Lipus