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公开(公告)号：US11182863B1
公开(公告)日：2021-11-23
申请号：US16362222
申请日：2019-03-22
Applicant: Facebook, Inc.
Inventor： Andrew Donald Yates , Kurt Dodge Runke , Gunjit Singh
IPC: G06F16/9535 , G06Q50/00
Abstract: An online system generates content feature entries, with each content feature entry describing a content item from a third party system. The online system generates user feature entries, each user feature entry describing a user. The online system generates a combination score for a target user and a selected content item by computing a combination of the content feature entries associated with the selected content item and the user feature entries associated with the target user using a combining function. The combination score indicates an estimated increase in value for the third party system when the target user is presented with the selected content item. The online system selects content items to transmit to a client device of a target user of the online system for presentation to the target user based on the combination score for the content items and the target user.
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公开(公告)号：US10242386B2
公开(公告)日：2019-03-26
申请号：US14970873
申请日：2015-12-16
Applicant: Facebook, Inc.
Inventor： Sue Ann Hong , Gunjit Singh , Kyle Edward Johnson , Atif Zahoor Khan
Abstract: An online system identifies seed users of high value to a sponsored content provider. Characteristics of the seed users are identified, and additional users having a threshold measure of similarity to the seed users are identified based on the characteristics. A score is determined for each of the additional users based on the measure of similarity. The seed users are placed in an initial tier of a tiered set of users for the sponsored content, and the additional users are placed in additional tiers of the tiered set of users based upon the determined scores such that each additional tier includes those users of the additional users having a specified range of determined scores, the tiers of the tiered set of users ranked according to the determined scores of users within each tier.
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公开(公告)号：US20180260736A1
公开(公告)日：2018-09-13
申请号：US15455051
申请日：2017-03-09
Applicant: Facebook, Inc.
Inventor： Andrew Donald Yates , Kurt Dodge Runke , Gunjit Singh
Abstract: When an opportunity arises to present a content item to a user, an online system delivers a content item to a user according to a first content delivery strategy associated with the content item. For the impression of the content item to the user, the online system tracks attributes associated with the first content delivery strategy. In addition to tracking the attributes associated with the first content delivery strategy, the online system also tracks attributes associated with at least one other content delivery strategy (a second content delivery strategy). The attributes tracked for the second content delivery strategy are used to train a machine learning model for the second content delivery strategy. The model is used to deliver the content item or other items according to the second content delivery strategy.
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公开(公告)号：US10936954B2
公开(公告)日：2021-03-02
申请号：US15447068
申请日：2017-03-01
Applicant: Facebook, Inc.
Inventor： Andrew Donald Yates , Gunjit Singh , Kurt Dodge Runke
Abstract: An online system generates predicted outcomes for a content distribution program that distributes content to users of the online system, the predicted outcome indicating a likelihood for the occurrence of an outcome of a content presentation. The online system transmits the one or more predicted outcomes to the third party system, and receives prediction improvement data from the third party system, the prediction improvement data indicating an adjustment to errors in the predicted outcomes based on a prediction by the third party system. The online system updates the properties of a content distribution program based on the prediction improvement data, the updated content distribution program causing the online system to generate new predicted outcomes based on the prediction improvement data in content presentation opportunities. The online system also transmits content to users of the online system based on the updated content distribution program.
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公开(公告)号：US20180253651A1
公开(公告)日：2018-09-06
申请号：US15447068
申请日：2017-03-01
Applicant: Facebook, Inc.
Inventor： Andrew Donald Yates , Gunjit Singh , Kurt Dodge Runke
Abstract: An online system generates predicted outcomes for a content distribution program that distributes content to users of the online system, the predicted outcome indicating a likelihood for the occurrence of an outcome of a content presentation. The online system transmits the one or more predicted outcomes to the third party system, and receives prediction improvement data from the third party system, the prediction improvement data indicating an adjustment to errors in the predicted outcomes based on a prediction by the third party system. The online system updates the properties of a content distribution program based on the prediction improvement data, the updated content distribution program causing the online system to generate new predicted outcomes based on the prediction improvement data in content presentation opportunities. The online system also transmits content to users of the online system based on the updated content distribution program.
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Patent Agency Ranking
公开(公告)号：US11106997B2
公开(公告)日：2021-08-31
申请号：US15721203
申请日：2017-09-29
Applicant: Facebook, Inc.
Inventor： Andrew Donald Yates , Gunjit Singh , Kurt Dodge Runke
Abstract: An online system uses multiple machine learning models to select content for providing to a user of the online system. Specifically, the online system trains a general model that intakes a first set of features and outputs predictions at a general level. The online system further trains a residual model that intakes a second set of features. The residual model predicts a residual (e.g., an error) of the predictions outputted by the general model. Therefore, the predicted residual from the residual model is combined with the prediction from the general model in order to correct for the over-generality of the general model. The online system may use the combined prediction to send content to users.
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公开(公告)号：US20190102693A1
公开(公告)日：2019-04-04
申请号：US15721189
申请日：2017-09-29
Applicant: Facebook, Inc.
Inventor： Andrew Donald Yates , Gunjit Singh , Kurt Dodge Runke
Abstract: An online system determines candidate parameter values to be used by a machine learning algorithm to train a machine learning model by saving historical datasets that include historical parameter searches and the performance of prior machine learning models that were trained on the historical parameters. Using the historical datasets, the online system identifies parameter predictors associated with a relation between candidate parameter values and properties of the training dataset that will be used to train the machine learning model. The online system trains the machine learning models according to the candidate parameter values and validates that the machine learning model is performing as expected. If the online system detects that the machine learning model is performing outside of an acceptable range, the online system determines new candidate parameter values and re-trains the machine learning model.
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公开(公告)号：US20180075367A1
公开(公告)日：2018-03-15
申请号：US15261746
申请日：2016-09-09
Applicant: Facebook, Inc.
Inventor： Andrew Donald Yates , Kurt Dodge Runke , Gunjit Singh
CPC classification number: G06N20/00 , G06Q30/0269 , G06Q50/01
Abstract: An online system, such as a social networking system, generates shared models for one or more clusters of categories. A shared model for a cluster is common to the categories assigned to the cluster. In this manner, the shared models are specific to the group of categories (e.g., selected content providers) in each cluster while requiring a reasonable computational complexity for the online system. The categories are clustered based on the performance of a model specific to a category on data for other categories.
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公开(公告)号：US10970750B1
公开(公告)日：2021-04-06
申请号：US16296112
申请日：2019-03-07
Applicant: Facebook, Inc.
Inventor： Sue Ann Hong , Gunjit Singh , Kyle Edward Johnson , Atif Zahoor Khan
Abstract: An online system identifies seed users of high value to a sponsored content provider. Characteristics of the seed users are identified, and additional users having a threshold measure of similarity to the seed users are identified based on the characteristics. A score is determined for each of the additional users based on the measure of similarity. The seed users are placed in an initial tier of a tiered set of users for the sponsored content, and the additional users are placed in additional tiers of the tiered set of users based upon the determined scores such that each additional tier includes those users of the additional users having a specified range of determined scores, the tiers of the tiered set of users ranked according to the determined scores of users within each tier.
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公开(公告)号：US20190102694A1
公开(公告)日：2019-04-04
申请号：US15721203
申请日：2017-09-29
Applicant: Facebook, Inc.
Inventor： Andrew Donald Yates , Gunjit Singh , Kurt Dodge Runke
Abstract: An online system uses multiple machine learning models to select content for providing to a user of the online system. Specifically, the online system trains a general model that intakes a first set of features and outputs predictions at a general level. The online system further trains a residual model that intakes a second set of features. The residual model predicts a residual (e.g., an error) of the predictions outputted by the general model. Therefore, the predicted residual from the residual model is combined with the prediction from the general model in order to correct for the over-generality of the general model. The online system may use the combined prediction to send content to users.
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