Microsoft Technology Licensing, LLC

Jiuling Wang

Samaneh Abbasi Moghaddam

Qing Li

Kunal Mukesh Cholera

Chunzhe Zhang

Lachlan Green

Chih Cheng Paul Yuan

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 debugging tool is provided that includes logging functionality to allow a machine learned model administrator to replay machine learned recommendation model executions in order to identify points of error, without the scaling difficulties that would be involved in logging all features used in every recommendation.

Debugging tool for recommendation systems

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Union City, CA, United States of America

Jiuling Wang