Yahoo! Inc.

Oath Inc.

Datong Chen

Qi He

Peiji Chen

Zhen Xia

Liang Wang

Jayavel Shanmugasundaram

Erik Nathan Vee

Shu Zhang

Herve Marcellini

Yi Mao

Haiyan Luo

Jiayi Wen

Long Ji Lin

Musen Wen

Shiyong Cheng

Konstantin Salomatin

Xiaojiang Cheng

A signature matrix is used to test each online event for inclusion of a user in a pre-generated lookalike audience. Locality Sensitive Hashing is used to compile the signature matrix to dramatically reduce memory storage requirements and processing time for each received online event. A technique of producing incremental signature matrices between generation of the full signature matrix helps to enable near-real time performance in processing each received event to see if a user associated with that event should be added to one of the lookalike audiences pre-generated by advertiser.

Methods and systems for near real-time lookalike audience expansion in ads targeting

Systems and methods for identifying users according to their activity are disclosed. The identification of a user includes accessing a user activity log having a plurality of identifiers and corresponding activity information for each identifier, determining identifiers having correlating activity information, and assigning identifiers having correlating activity information to a common user.

Identifying users' identity through tracking common activity

Systems and methods for automatically forecasting the future availability of one or more resources, such as Internet advertising opportunities, are described herein. In accordance with various embodiments, a forecasting model that accounts for event-driven resource availability is trained based both on historical supply data and calendar information specifying events and event duration. The trained forecasting model is then used to forecast the availability of resources at one or more specified future time periods. In accordance with certain embodiments, the forecasting model comprises a Gaussian process model that has an event-driven kernel as a covariance function.

Embedding calendar knowledge in event-driven inventory forecasting

Techniques are described herein for using an impression-trend technique to provide a display advertising supply forecast. A display advertising supply forecast is an estimate of a number of impressions, which are to occur in a future time period, that have specified attribute values. For example, the specified attribute values may be descriptive of impressions with respect to which an advertiser wishes to place ads. An impression-trend technique is a forecasting technique that uses trends regarding past impressions to forecast a number of future impressions that have specified attribute values. The past impressions include attribute values that are related to the specified attribute values.

Impression-trend techniques for providing a display advertising supply forecast

Growing community of inventors

Santa Clara, CA, United States of America

Datong Chen