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    Hierarchical feature selection and predictive modeling for estimating performance metrics
    1.
    发明授权
    Hierarchical feature selection and predictive modeling for estimating performance metrics 有权

    公开(公告)号：US11080764B2

    公开(公告)日：2021-08-03

    申请号：US15458484

    申请日：2017-03-14

    Applicant: ADOBE INC.

    Inventor： Chen Dong ,  Zhenyu Yan ,  Pinak Panigrahi ,  Xiang Wu ,  Abhishek Pani

    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.

    Prediction of content performance in content delivery based on presentation context
    2.
    发明授权
    Prediction of content performance in content delivery based on presentation context 有权

    公开(公告)号：US10748178B2

    公开(公告)日：2020-08-18

    申请号：US14874298

    申请日：2015-10-02

    Applicant: ADOBE INC.

    Inventor： Zhenyu Yan ,  Xiang Wu ,  Chen Dong ,  Abhishek Pani

    IPC: G06Q30/00 ,  G06Q30/02

    Abstract: In various implementations, analytics data is received that indicates performance of bid targets for historical bids made in one or more content delivery auctions. Baseline prediction models are maintained for the bid targets. The baseline prediction models use the analytics data to predict performance of the bid targets in one or more future instances of at least one content delivery auction. A presentation context factor model is maintained that provides an adjustment factor that quantifies a contribution of a subset of a plurality of presentation context factors associated with the bid targets to performance of the bid targets based on predicted values from the baseline prediction models. A contextual predicted value is computed using the adjustment factor for the subset of the plurality of presentation context factors. A performance prediction is transmitted to a user device and is based on at least the contextual predicted value.

    ACTIONABLE KPI-DRIVEN SEGMENTATION
    4.
    发明申请
    ACTIONABLE KPI-DRIVEN SEGMENTATION 审中-公开

    公开(公告)号：US20200151746A1

    公开(公告)日：2020-05-14

    申请号：US16191289

    申请日：2018-11-14

    Applicant: ADOBE INC.

    Inventor： Xiang Wu ,  Zhenyu Yan ,  Yi-Hong Kuo ,  Wuyang Dai ,  Polina Bartik ,  Abhishek Pani

    IPC: G06Q30/02 ,  G06Q10/06 ,  G06F17/16

    Abstract: An improved analytics system generates actionable KPI-based customer segments. The analytics system determines predicted outcomes for a key performance indicator (KPI) of interest and a contribution value for each variable indicating an extent to which each variable contributes to predicted outcomes. Topics are generated by applying a topic model to the contribution values for the variables. Each topic comprises a group of variables with a contribution level for each variable that indicates the importance of each variable to the topic. User segments are generated by assigning each user to a topic based on attribution levels output by the topic model.

    Survival Analysis Based Classification Systems for Predicting User Actions
    5.
    发明申请
    Survival Analysis Based Classification Systems for Predicting User Actions 审中-公开

    公开(公告)号：US20200065713A1

    公开(公告)日：2020-02-27

    申请号：US16112546

    申请日：2018-08-24

    Applicant: Adobe Inc.

    Inventor： Xiang Wu ,  Zhenyu Yan ,  Yi-Hong Kuo ,  Wuyang Dai ,  Julia Viladomat Comerma ,  Abhishek Pani

    IPC: G06N99/00

    Abstract: Techniques and systems are described that employ survival analysis and classification to predict occurrence of future events by a digital analytics system. Survival analysis involves modeling time to event data. Survival analysis is used by digital analytics systems to analyze an expected duration of time until an event happens. In the techniques described herein, survival analysis is employed as part of a classification technique by a digital analytics system. In one example, a digital analytics system generates training data from a dataset in accordance with a survival analysis technique such that, after generated, the training data is usable to train a classification model.

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