Microsoft Technology Licensing, LLC

Kunal Mukesh Cholera

Anthony Duane Duerr

Santosh Kumar Kancha

Kumaresh Pattabiraman

Keren Baruch

Qing Li

Chunzhe Zhang

Jiuling Wang

Jianbao Tao

Dan Xu

In an example embodiment, a machine learned model that integrates a generalized linear mixed model (GLMix) non-linear optimization is utilized to jointly perform personalized communications targeting and volume control. The machine learned model may be trained to not only maximize user engagement with a notification generally, such as maximizing the total number of people who view, save, or apply for a job associated with a job listing in the communication, but also trained to maximize an end goal of the notification, such as the total number of people who apply for the job associated with a job listing in the communication.

Integrated GLMix and non-linear optimization architectures

In an example embodiment, a fast and space-efficient ranking mechanism is used for vertical searches. A precedence is assigned to a plurality of features based on the relative importance of those features to the provider of a vertical search engine. In an example embodiment, this relative importance may be explicitly indicated by the provider of the vertical search engine. A unique data structure may then be created that incorporates the precedence of each feature.

Precedence-based fast and space-efficient ranking

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Mountain View, CA, United States of America

Kunal Mukesh Cholera