Yahoo Ad Tech LLC

Microsoft Technology Licensing, LLC

Oath Inc.

Indeed, Inc.

Haiyan Luo

Yi Mao

Herve Marcellini

John Cheng

Xiang Yu

Shu Zhang

Hardik Bati

John P Moore

Datong Chen

Swetha Nagabhushan Karthik

Zhen Xia

Jiayi Wen

Anand Joseph Bernard Selvaraj

Shiyong Cheng

Shichuan Ma

Xiaojiang Cheng

Yu Sun

The present teaching relates to tracking an event at a plurality of distributed servers. In one example, an event to be tracked is determined. A user associated with the event is identified. A script is generated to be embedded in a web page. The script triggers an event message when the user performs an online behavior related to the web page in accordance with the event. The event message triggered by the script is received. A tracing flag is determined from the event message. An instruction is provided to the plurality of distributed servers for executing one or more applications based on the event and the tracing flag.

Method and system for tracking events in distributed high-throughput applications

An artificial intelligence (AI) job recommender system and methods implement neural network machine learning by generating and utilizing actual and synthetic training data to identify, learn, and apply latent job-to-job transition information and trends to improve job recommendations. The AI job recommender system and method represent technological advances that, for example, identify data representations, identify multiple instances of latent information in actual data, develop synthetic training data, create a directed graph from latent, directional information, embed the directed graph into a vector space, and apply machine learning algorithms to technologically advance and transform a machine into a specialized machine that learns and improves job recommendations across the vector space.

Artificial intelligence job recommendation neural network machine learning training based on embedding technologies and actual and synthetic job transition latent information

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

Techniques are provided for server-to-server interaction for content item selection. A system receives a request for one or more content items from a client device. In response to receiving the request, the system generates multiple content requests. Each content request is sent to a different content provider of multiple content providers. At least one of the content requests is transmitted over a network to a content provider that is remote relative to the system. The system receives multiple responses, each of which is from a different content provider of the multiple content providers. Each response identifies a different content item. The system makes a determination that at least one content item is associated with a particular web domain. The system selects, based on the determination, a particular content item of the plurality of content items and causes the particular content item to be transmitted to the client device.

Server to server interaction in content item selection events

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Haiyan Luo