Invention Grant
- Patent Title: Hierarchical feature selection and predictive modeling for estimating performance metrics
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Application No.: US15458484Application Date: 2017-03-14
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Publication No.: US11080764B2Publication Date: 2021-08-03
- Inventor: Chen Dong , Zhenyu Yan , Pinak Panigrahi , Xiang Wu , Abhishek Pani
- Applicant: ADOBE INC.
- Applicant Address: US CA San Jose
- Assignee: ADOBE INC.
- Current Assignee: ADOBE INC.
- Current Assignee Address: US CA San Jose
- Agency: Shook, Hardy & Bacon L.L.P.
- Main IPC: G06Q30/02
- IPC: G06Q30/02 ; G06N7/00 ; G06F16/951 ; G06F16/245 ; G06Q30/08

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.
Public/Granted literature
- US20180268444A1 HIERARCHICAL FEATURE SELECTION AND PREDICTIVE MODELING FOR ESTIMATING PERFORMANCE METRICS Public/Granted day:2018-09-20
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