Microsoft Technology Licensing, LLC

Divyakumar Menghani

Yue Li

Tomer Cohen

Vibhu Prakash Saxena

Rajan Ramesh Chaudhari

Ming Chen

Yiran Pang

Aaron Tyler Rucker

Kuo-Ning Huang

Sparsh Agarwal

The technical problem of predicting the level of activity driven by a web page representing a company in an on-line social network system is addressed by providing a company page assessment system. A company page assessment system is configured to use a machine learning model to determine features and respective thresholds that signal the likelihood of a web page representing a company (a company page) to drive member activity in the on-line social network system. The company page assessment system also provides the ability to assess quality of a company page. Different quality levels of a company page can be indicated by respective labels 'Complete Page,' 'Quality Page,' and &#x201c;Engaged Quality Page.&#x201d;

Capturing company page quality

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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Sunnyvale, CA, United States of America

Divyakumar Menghani