Xian Xu resumes & CV records
Resumes
Xian Zhu Xu, 7110518 Mcclellan Pl, Cupertino, CA 95014
Xian Xu Phones & Addresses
Cupertino, CA
San Jose, CA
Sunnyvale, CA
Sandusky, IA
Austin, TX
Camarillo, CA
10518 Mcclellan Pl, Cupertino, CA 95014
Mentions for Xian Zhu Xu
Publications & IP owners
Us Patents
Advertisement Conversion Prediction Based On Unlabeled Data
US Patent:
2017028, Oct 5, 2017
Filed:
Apr 5, 2016
Appl. No.:
15/091105
Inventors:
- Menlo Park CA, US
Pradheep K. Elango - Mountain View CA, US
Xian Xu - Menlo Park CA, US
Pradheep K. Elango - Mountain View CA, US
Xian Xu - Menlo Park CA, US
International Classification:
G06Q 30/02
G06N 99/00
G06N 7/00
G06N 99/00
G06N 7/00
Abstract:
Embodiments are disclosed for predicting target events occurrence for an advertisement campaign. A computing device according to some embodiments assigns a label to an advertisement as unlabeled, in response to a notification that a prerequisite event occurs for the advertisement. The device generates feature vectors based on data that relate to the advertisement. The device further trains a machine learning model using the feature vectors of the unlabeled advertisement based on a first term of an objective function, without waiting for a target event for the advertisement to occur. The first term depends on unlabeled advertisements. The device predicts a probability of a target event occurring for a new advertisement, by feeding data of the new advertisement to the trained machine learning model.
Lookalike Evaluation
US Patent:
2017014, May 18, 2017
Filed:
Nov 13, 2015
Appl. No.:
14/941495
Inventors:
- Menlo Park CA, US
Xian Xu - Menlo Park CA, US
Yang Pei - Menlo Park CA, US
Xian Xu - Menlo Park CA, US
Yang Pei - Menlo Park CA, US
International Classification:
G06N 5/04
G06N 99/00
G06N 99/00
Abstract:
Lookalike models can select users that are predicted to share characteristics with a specified set of seed users. The processing requirements for lookalike models can be decreased by identifying features that have low impact on model accuracy, and therefore can be excluded from creating models. Also, by identifying preferred seed sources and training parameters, accurate lookalike models can be created with less overhead and in less time. The features and training parameters can be identified by obtaining a sample seed set, extracting seeds with a defined set of features, and using the remaining training seeds to train a model. Performance of this model can be compared to a standard model to see if the model performs well. If so, features excluded from the features used to create the model, a seed source, or training parameters used to create the model can be selected.
Possible Relatives
NOTICE: You may not use BackgroundCheck or the information it provides to make decisions about employment, credit, housing or any other purpose that would require Fair Credit Reporting Act (FCRA) compliance. BackgroundCheck is not a Consumer Reporting Agency (CRA) as defined by the FCRA and does not provide consumer reports.