Microsoft Technology Licensing, LLC

Vibhu Prakash Saxena

Yue Li

Yan Liu

Chi-Yi Kuan

Huan Hoang

Keqing Liang

Kuo-Ning Huang

Ming Chen

Divyakumar Menghani

Yiran Pang

Jason Phan

Burcu Baran

Techniques for predicting relevance of social networking service member accounts to a job posting. In an embodiment, a candidate predictor engine of a system encodes data representing an applicant quality (AQ) score for each job/applicant pair for a plurality of applicants to a job posting. Additionally, the system stores the encoded data and assigns member-level weights to each of the applicants. Moreover, the system calculates weighted AQ scores for each of the job/applicant pairs, the weighted AQ scores being products of respective AQ scores and member-level weights. Furthermore, the system sums the weighted AQ scores to derive a total weighted score for the job posting. Then, the candidate predictor engine generates a job-level probability of confirmed hire (pCH) based on the total weighted score, the job-level pCH indicating a likelihood of the posting being filled by an applicant. Also, the system transmits the job-level pCH to a client for display.

Job applicant probability of confirmed hire

A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Key Feature Engine receives a request for a desired number of key features to be identified from a data set in at least a portion of a database. The Key Feature Engine executes instances of multiple types of machine learning data models on the data set to calculate respective regression coefficients, in each machine learning data model instance, for each feature in a plurality of features defined by a plurality of data categories of a social network service. The Key Feature Engine identifies at least one key feature, of one or more instances of the multiple types of machine learning data models, based on a value of a corresponding regression coefficient.

Understanding business insights and deep-dive using artificial intelligence

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

Vibhu Prakash Saxena