Microsoft Technology Licensing, LLC

Kuo-Ning Huang

Yan Liu

Yue Li

Vibhu Prakash Saxena

Song Lin

Yiran Pang

Ming Chen

Boyu Zhang

Divyakumar Menghani

Yunjing Zhang

Techniques are provided for using machine learning techniques to identify predictive features and predictive values for each feature. In one technique, a model is trained based on training data that comprises training instances, each of which corresponds to multiple usage-based features of an online service by a user. For each usage-based feature in a subset of the usage-based features, the model is used to generate a dependency graph, a histogram is generated, and an optimized value is selected based on the dependency graph and the histogram. A user of the online service is identified, along with a usage value that indicates a level of usage, by the user, of a usage-based feature. A comparison between the usage value and an optimized value of the usage-based feature is performed. Based on the comparison, it is determined whether to present data about that usage-based feature to the user.

Machine learning techniques to identify predictive features and predictive values for each feature

Techniques for searching for generating and using an online activity index for users of an online service are disclosed herein. In some example embodiments, for each one of a plurality of dimensions of online activity for an online service, the system determines a corresponding value for each one of a set of one or more activities corresponding to the dimension for a user of the online service, with the value representing a level of engagement by the user in the corresponding activity via the online service. The system generates a corresponding activity index for each one of the dimensions based on the corresponding value for each one of the set of one or more activities corresponding to the dimension. The system generates content based on the corresponding activity index of at least one of the dimensions, and causes the content to be displayed on a device associated with the user.

Online activity index

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Los Altos, CA, United States of America

Kuo-Ning Huang