Aol Advertising Inc.

Mercury Kingdom Assets Limited

Yahoo Ad Tech LLC

Oath Inc.

Yahoo Assets LLC

Oath (americas) Inc.

Jianlong Zhang

Niklas Karlsson

Jaime Batiz-Mendoza

Brendan Kitts

Liang Wang

Aaron Eliasib Flores

Junwei Pan

Tian Zhou

Liang Peng

Shengjun Pan

Djordje Gligorijevic

Balaji Srinivasa Rao Paladugu

Bharatbhushan Shetty

Sneha Thomas

Xianen Qiu

Tingyu Mao

Hao He

San Gultekin

Robert Luenberger

Li Si Yang

Scott Strickland

Yih-Shin Huang

Jamie Batiz-Mendoza

Yonggang Xu

Seyed Mohammad Ziaemohseni

Hans W Uhlig

Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Bid value determination for a first-price auction

One or more computing devices, systems, and/or methods for assessing riskiness of a domain are provided. For example, a content request is received from a content provider service that hosts a website associated with a domain. The content request is evaluated to identify request features. Feature scores are assigned to the request features using labeled feature data. The feature scores are aggregated to generate a content request risk score corresponding to a riskiness of the content request corresponding to fraud, such as domain spoofing. The content request risk score along with other content request risk scores of content requests associated with the content provider service are aggregated to create a content provider risk score corresponding to a riskiness of the content provider service, such as a risk of the domain being fraudulent. The content provider risk score is used to either block or process the content request.

Systems and methods for assessing riskiness of a domain

Systems and methods are provided for controlling an online advertising campaign. In one embodiment, a computer-implemented method for controlling an online advertising campaign includes receiving a feedback signal reflecting delivery of the online advertising campaign, and comparing the feedback signal to a delivery reference to generate a campaign level control signal. The method further includes receiving a maximum impression bid price for an inventory unit of the online advertising campaign, the maximum bid price for the at least one inventory unit being set by a user, and calculating, using at least one processor, at least a final bid price based on the maximum bid price, on the campaign level control signal, and on an optimization objective for the online advertising campaign, the optimization objective being set by the user. The method also includes submitting, to an electronic market and based on the calculated final bid price, a bid on an impression from the inventory unit.

Systems and methods for controlling online advertising campaigns

In one embodiment, a computer-implemented method for controlling an online advertising campaign includes receiving a feedback signal reflecting delivery of the online advertising campaign, and comparing the feedback signal to a delivery reference to generate a campaign level control signal. The method further includes receiving a maximum impression bid price for an inventory unit of the online advertising campaign, the maximum bid price for the at least one inventory unit being set by a user, and calculating, using at least one processor, at least a final bid price based on the maximum bid price, on the campaign level control signal, and on an optimization objective for the online advertising campaign, the optimization objective being set by the user. The method also includes submitting, to an electronic market and based on the calculated final bid price, a bid on an impression from the inventory unit.

System and methods are provided for analyzing and displaying digital content and advertisements over electronic networks. In accordance with one implementation, a method is provided that calculates, using at least one processor, an engagement metric (including, for example, click probability) for a specific combination of time, promotion slot, and user segment, for an individual promotion of a set of promotions, and calculates a value of a landing page associated with the promotion. The method then can multiply the engagement metric and the value to determine the valuation of the promotion. The method can operate recursively by using previous calculations in later runs of the same method.

Systems and methods for displaying digital content and advertisements over electronic networks

In accordance with the invention, a system, method, and apparatus for analyzing advertisement-related data are presented, which may include receiving data related to an aspect of an advertisement and modeling the aspect of the advertisement with a mathematical model. The mathematical model may include a control-signal-related component, a control-signal-independent component, and an error component. Each component may be updated based on at least one of a control signal, the received data, and a previous state of at least one of the components. An updated model may be created base on the updated components. The system, method, and apparatus may also include predicting the aspect of the advertisement using the updated model. Exemplary aspects of and data related to the advertisement may include one or more of the following: a number of impressions, 'clicks,' or 'conversions' and/or the impression-to-conversion, impression-to-click, or click-to-conversion ratios.

System identification, estimation, and prediction of advertising-related data

In accordance with the invention, a system, method, and apparatus for analyzing advertisement-related data are presented, which may include receiving data related to an aspect of an advertisement and modeling the aspect of the advertisement with a mathematical model. The mathematical model may include a control-signal-related component, a control-signal-independent component, and an error component. Each component may be updated based on at least one of a control signal, the received data, and a previous state of at least one of the components. An updated model may be created based on the updated components. The system, method, and apparatus may also include predicting the aspect of the advertisement using the updated model. Exemplary aspects of and data related to the advertisement may include one or more of the following: a number of impressions, 'clicks,' or 'conversions' and/or the impression-to-conversion, impression-to-click, or click-to-conversion ratios.

System identification estimation and prediction of advertising-related data

Methods and systems consistent with the present invention enable the control of aspects of an advertising campaign, such as revenue expenditure, by comparing actual and desired behavior and incorporating feedback and feed-forward error correction to continuously adjust the campaign's behavior. In certain embodiments, this control is achieved without considering individual advertisement requests or ad placements, thus bypassing the lowest-level details and instead adjusting behavior on a campaign-wide level using an adaptive control system.

Methods for controlling an advertising campaign

Growing community of inventors

Mountain View, CA, United States of America

Jianlong Zhang