Facebook, Inc.

Whatsapp Inc.

Xian Xu

Payman Mohassel

Pradheep K Elango

Shuangying Huang

Haibin Cheng

Sundararaman Jeyaraman

Ananth Raghunathan

Subodh Iyengar

Chen-Kuei Lee

Sameer Indarapu

Zutian Luo

Shaahid Shaik

Yang Pei

Liuqing Albert Zhang

Yen-Chieh Sung

A system and method comprising collecting analytics data or other types of analytics-related information from client devices in an anonymous and authenticated manner. A client device may use or provide a token or token-relation information, which may have been obtained from system servers beforehand, with a request to log the analytics data to the system servers in order to authenticate the analytics data being logged. The system servers may then authenticate the token or the token-related information, and in response to successful authentication, process the analytics data. Advantageously, the use of the token or token-related information allows the analytics data to be logged by client devices anonymously to preserve privacy, but also in a highly secure manner.

Authenticating anonymous information

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.

Advertisement conversion prediction based on unlabeled data

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.

Lookalike evaluation

Growing community of inventors

Menlo Park, CA, United States of America

Xian Xu