Microsoft Corporation
PROBABILISTIC SYSTEMS AND ARCHITECTURE TO PREDICT AND OPTIMIZE HIRES

Abstract:

Techniques are provided for implementing a probabilistic system and architecture to predict and optimize particular user activity. In one technique, opportunity application data that indicates multiple applications to multiple opportunities is stored. Tracking data that indicates, for each opportunity, a number of reviewer actions with respect to applications to the opportunity is stored. Based on the tracking data, one or more machine learning techniques are used to learn parameters of a model that takes, as input, a number of weighted applications of an opportunity and generates, as output, a prediction of a confirmed hire for the opportunity. A particular opportunity is identified and a first number of reviewer actions with respect to the particular opportunity is determined. A second number of weighted applications for the particular opportunity is generated based on the first number. The second number is input into the model to generate a score for the particular opportunity.