公开(公告)号：US20200178026A1

公开(公告)日：2020-06-04

申请号：US16780802

申请日：2020-02-03

申请人： xAd, Inc.

发明人： Can Liang ,  Yilin Chen ,  Weiqing Yu ,  Fan Yang

IPC分类号： H04W4/021 ,  G06F16/29 ,  G06N20/00 ,  H04L29/08

摘要： A system for predicting a conversion rate relating to targeted location events for a test campaign includes one or more campaign databases configured to store campaign parameters of a set of historical campaigns. The system further includes a feature engineering module configured to construct a training feature space corresponding to the set of historical campaigns, and to determine a set of labels including a respective conversion rate for each respective historical campaign of the plurality of historical campaigns. The system further includes a model training module configured to machine train a conversion rate prediction model using the training feature space and the set of labels. The feature engineering module is further configured to construct a set of test features from campaign parameters of the test campaign, and the system further includes a prediction module configured to apply the conversion rate prediction model to the set of test features to obtain a predicted conversion rate for the test campaign.

公开(公告)号：US11172324B2

公开(公告)日：2021-11-09

申请号：US16780802

申请日：2020-02-03

申请人： xAd, Inc.

发明人： Can Liang ,  Yilin Chen ,  Weiqing Yu ,  Fan Yang

IPC分类号： H04W4/021 ,  H04L29/08 ,  G06N20/00 ,  G06F16/29

摘要： A system for predicting a conversion rate relating to targeted location events for a test campaign includes one or more campaign databases configured to store campaign parameters of a set of historical campaigns. The system further includes a feature engineering module configured to construct a training feature space corresponding to the set of historical campaigns, and to determine a set of labels including a respective conversion rate for each respective historical campaign of the plurality of historical campaigns. The system further includes a model training module configured to machine train a conversion rate prediction model using the training feature space and the set of labels. The feature engineering module is further configured to construct a set of test features from campaign parameters of the test campaign, and the system further includes a prediction module configured to apply the conversion rate prediction model to the set of test features to obtain a predicted conversion rate for the test campaign.