Abstract: 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.

Abstract: 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.

Abstract: Systems and methods of evaluating information in a computer network environment are provided. A data processing system can obtain or receive a content placement criterion, such as a keyword, associated with a content item and can determine a quality metric of the content placement criterion. The data processing system can identify a candidate content placement criterion and expand placement criteria associated with the content item to include the content placement criterion and the candidate content placement criterion based at least in part on an evaluation of the quality metric of the content placement criterion. The data processing system can expand placement criteria based in part on a throttling parameter. The data processing system can identify a correlation between a document and the placement criteria to identify appropriate content items for the document.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for providing network activity performance data in a content infrastructure. Instructions stored in the system or apparatus, when executed by processors, cause the processors to: identify a content item provided by a content provider device; calculate a set of keyword scores for the set of keywords, each keyword score calculated to measure a relationship between a semantic word cluster of the keyword and a semantic word cluster of an other content for which the content item was previously selected; determine an effect that a particular keyword of the set of keywords has on the online content selection process based on the set of keyword scores; generate a report indicating the effect that the particular keyword has on the online content selection process; and provide, via a network, the report to a client device.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for advertisement keyword scoring. A processing circuit receives a request for an advertisement to be provided to a user during a user session. The advertisement is to be provided alongside other content that is associated with a first plurality of keywords. A processing circuit identifies a plurality of advertisements based on the first plurality of keywords. Each of the plurality of advertisements are associated with a second plurality of keywords. The processing circuit calculates a keyword score for each of the second plurality of keywords for each of the plurality of advertisements. Based on the keyword score, one of the keywords for each of the plurality of the plurality of advertisements is selected. Based on a comparison of the selected keywords, the advertisement to be provided to the user is selected.

Abstract: The invention discloses a method for obtaining the geometrical and mechanical parameters of rock samples and a holographic scanning system thereof, wherein the system includes an observation mechanism, a multi-scale penetration mechanism, a grinding mechanism, a rock sample installation mechanism arranged on a three-axis precision motion platform, and an industrial computer controlling the operation mode of each mechanism of the platform Indentation/rotary penetration test, pulse echo signal acquisition, three-dimensional surface topography reconstruction, layer by layer grinding and repeated experiments are carried out. The geometric parameters and corresponding mechanical field parameters are obtained by spatial interpolation of the three-dimensional parameter lattice accumulated by several layers of single-layer rock parameters. The holographic scanning system and method can obtain the real spatial distribution of various media in rock samples.

Abstract: Systems and methods of evaluating information in a computer network environment are provided. A data processing system can obtain or receive a content placement criterion, such as a keyword, associated with a content item and can determine a quality metric of the content placement criterion. The data processing system can identify a candidate content placement criterion and expand placement criteria associated with the content item to include the content placement criterion and the candidate content placement criterion based at least in part on an evaluation of the quality metric of the content placement criterion. The data processing system can expand placement criteria based in part on a throttling parameter. The data processing system can identify a correlation between a document and the placement criteria to identify appropriate content items for the document.

Abstract: The invention discloses a method for obtaining the geometrical and mechanical parameters of rock samples and a holographic scanning system thereof, wherein the system includes an observation mechanism, a multi-scale penetration mechanism, a grinding mechanism, a rock sample installation mechanism arranged on a three-axis precision motion platform, and an industrial computer controlling the operation mode of each mechanism of the platform Indentation/rotary penetration test, pulse echo signal acquisition, three-dimensional surface topography reconstruction, layer by layer grinding and repeated experiments are carried out. The geometric parameters and corresponding mechanical field parameters are obtained by spatial interpolation of the three-dimensional parameter lattice accumulated by several layers of single-layer rock parameters. The holographic scanning system and method can obtain the real spatial distribution of various media in rock samples.

Abstract: Systems and methods of evaluating information in a computer network environment are provided. A data processing system can obtain or receive a content placement criterion, such as a keyword, associated with a content item and can determine a quality metric of the content placement criterion. The data processing system can identify a candidate content placement criterion and expand placement criteria associated with the content item to include the content placement criterion and the candidate content placement criterion based at least in part on an evaluation of the quality metric of the content placement criterion. The data processing system can expand placement criteria based in part on a throttling parameter. The data processing system can identify a correlation between a document and the placement criteria to identify appropriate content items for the document.

Abstract: 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.

Abstract: 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.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for advertisement keyword scoring. A processing circuit receives a request for an advertisement to be provided to a user during a user session. The advertisement is to be provided alongside other content that is associated with a first plurality of keywords. A processing circuit identifies a plurality of advertisements based on the first plurality of keywords. Each of the plurality of advertisements are associated with a second plurality of keywords. The processing circuit calculates a keyword score for each of the second plurality of keywords for each of the plurality of advertisements. Based on the keyword score, one of the keywords for each of the plurality of the plurality of advertisements is selected. Based on a comparison of the selected keywords, the advertisement to be provided to the user is selected.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for advertisement keyword scoring. A processing circuit receives a request for an advertisement to be provided to a user during a user session. The advertisement is to be provided alongside other content that is associated with a first plurality of keywords. A processing circuit identifies a plurality of advertisements based on the first plurality of keywords. Each of the plurality of advertisements are associated with a second plurality of keywords. The processing circuit calculates a keyword score for each of the second plurality of keywords for each of the plurality of advertisements. Based on the keyword score, one of the keywords for each of the plurality of the plurality of advertisements is selected. Based on a comparison of the selected keywords, the advertisement to be provided to the user is selected.

Abstract: 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.

Abstract: Systems and methods of evaluating information in a computer network environment are provided. A data processing system can obtain or receive a content placement criterion, such as a keyword, associated with a content item and can determine a quality metric of the content placement criterion. The data processing system can identify a candidate content placement criterion and expand placement criteria associated with the content item to include the content placement criterion and the candidate content placement criterion based at least in part on an evaluation of the quality metric of the content placement criterion. The data processing system can expand placement criteria based in part on a throttling parameter. The data processing system can identify a correlation between a document and the placement criteria to identify appropriate content items for the document.

Abstract: 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.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for advertisement keyword scoring. A processing circuit receives a request for an advertisement to be provided to a user during a user session. The advertisement is to be provided alongside other content that is associated with a first plurality of keywords. A processing circuit identifies a plurality of advertisements based on the first plurality of keywords. Each of the plurality of advertisements are associated with a second plurality of keywords. The processing circuit calculates a keyword score for each of the second plurality of keywords for each of the plurality of advertisements. Based on the keyword score, one of the keywords for each of the plurality of the plurality of advertisements is selected. Based on a comparison of the selected keywords, the advertisement to be provided to the user is selected.

Abstract: Systems and methods of evaluating information in a computer network environment are provided. A data processing system can obtain or receive a content placement criterion, such as a keyword, associated with a content item and can determine a quality metric of the content placement criterion. The data processing system can identify a candidate content placement criterion and expand placement criteria associated with the content item to include the content placement criterion and the candidate content placement criterion based at least in part on an evaluation of the quality metric of the content placement criterion. The data processing system can expand placement criteria based in part on a throttling parameter. The data processing system can identify a correlation between a document and the placement criteria to identify appropriate content items for the document.

Abstract: 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.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scoring criteria for content items. In one aspect, a method includes identifying a primary ranking signal and a set of auxiliary ranking signals for ranking a set of criteria for a content item. A primary score and a set of auxiliary scores can be identified for each particular criterion in the set of criteria. Each auxiliary score can be adjusted to generate adjusted auxiliary scores. The adjusting can include applying, to at least a portion of the auxiliary scores, a transformation function that reduces an amount of skewness among the auxiliary scores. A ranking score can be determined for each particular criterion based on a function of the primary score for the particular criterion and the adjusted auxiliary scores.