US 11,080,764 B2Granted

Hierarchical feature selection and predictive modeling for estimating performance metrics

AssigneeAdobe Inc.

InventorsChen Dong, Zhenyu Yan, Pinak Panigrahi, Xiang Wu, Abhishek Pani

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Abstract

A bid management system generates estimated performance metrics at the bid unit level to facilitate bid optimization. The bid management system includes a hierarchical feature selection and prediction approach. Feature selection is performed by aggregating historical performance metrics to a higher hierarchical level and testing features for statistical significance. Features for which a significance level satisfies a significance threshold are selected for prediction analysis. The prediction analysis uses a statistical model based on selected features to generate estimated performance metrics at the bid unit level. In some implementations, the prediction analysis uses a hierarchical Bayesian smoothing method in which estimated performance metrics are calculated at the bid unit level using a posterior probability distribution derived from a prior probability distribution determined based on aggregated performance metrics and a likelihood function that takes into account historical performance metrics from the bid unit level based on the selected features.

Claims20

Dependent claims

A computer system comprising:

a processor;
and
a computer storage medium storing computer-useable instructions that, when used by the processor, cause the processor to:
perform feature selection to generate a machine learning model, the feature selection being done by:
aggregating, by the processor executing a feature selection component, performance metrics of each feature, of a plurality of features, for a plurality of bid units from a bid unit hierarchical level to a first hierarchical level to generate aggregated performance data, the first hierarchical level comprising a hierarchical level above the bid unit hierarchical level within a hierarchical structure;
in response to the aggregating of the performance metrics, employing, by the processor executing the feature selection component, a statistical testing function to determine a significance level for each feature, of the plurality of features, at the first hierarchical level using the aggregated performance data;
and
comparing, by the processor executing the feature selection component, the significance level at the first hierarchical level for each feature to a significance threshold;
based on determining that the significance level for a first feature, of the plurality of features, at the first hierarchical level satisfies the significance threshold, generating, by the processor executing the feature selection component, the machine learning model using the first feature as input;
based on determining that the significance level for a second feature, of the plurality of features, at the first hierarchical level fails to satisfy the significance threshold, excluding the second feature from being used as input into the machine learning model;
performing, by the processor executing a prediction component, a prediction analysis using the first feature and the machine learning model based at least in part on the aggregating of the performance metrics, the generating of the machine learning model, and the excluding of the second feature;
and
in response to the performing of the prediction analysis, causing, by the processor executing a SEM management UI component, a user interface to be generated, the user interface indicates the feature and the prediction analysis.

10 more elements

A computer storage medium storing computer-useable instructions that, when executed by a computing device, cause the computing device to perform operations, the operations comprising:

performing feature selection to select a feature of a plurality of features for a plurality of bid units by aggregating, by the computing executing a feature selection component, performance metrics for each feature for the plurality of bid units from a bid unit hierarchical level to a first hierarchical level and, the first hierarchical level comprising a hierarchical level above the bid unit hierarchical level within a hierarchical structure;
employing, by the computing device executing the feature selection component, a statistical testing function to determine a significance level for each feature, of the plurality of features, at the first hierarchical level using the aggregated performance statistics;
based on determining that the significance level for a first feature, of the plurality of feature at the first hierarchical level exceeds a threshold and that a second feature, of the plurality of features, fails to exceed the threshold, generating, by the computing device executing the feature selection component, a model with the first feature as input, wherein the second feature is excluded from being used by the model as input;
in response to the generating of the model with the first feature as input, performing, by the computing device executing a prediction component, prediction analysis by using hierarchical Bayesian smoothing to employ the aggregated performance metrics as a prior probability distribution to generate estimated performance metrics for each bid unit.

1 more element

Dependencies are inferred from the claim text.