Abstract: In an example embodiment, a specialized machine learned model, called a look-alike model, is trained using a machine learning algorithm to predict future job engagement for a user. This look-alike model is then used to create new segments on top of the segments provided by a rules-based approach. Specifically, the look-alike model is designed to take users who have been segmented by a rule-based approach into an “inactive job seeker” categorization (such as those assigned to the resting users and dormant users segments) and calculate a predicted job engagement score for these users. Based on the predicted job engagement score, a user may then be reassigned from one of the inactive job seeker categorizations to one of one or more new job seeker categorizations (such as predicted open job seekers or predicted opportunistic job seekers).

Abstract: 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.

Abstract: In an example embodiment, a specialized machine learned model, called a look-alike model, is trained using a machine learning algorithm to predict future job engagement for a user. This look-alike model is then used to create new segments on top of the segments provided by a rules-based approach. Specifically, the look-alike model is designed to take users who have been segmented by a rule-based approach into an “inactive job seeker” categorization (such as those assigned to the resting users and dormant users segments) and calculate a predicted job engagement score for these users. Based on the predicted job engagement score, a user may then be reassigned from one of the inactive job seeker categorizations to one of one or more new job seeker categorizations (such as predicted open job seekers or predicted opportunistic job seekers).

Abstract: 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.

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.

Abstract: A technical problem of multi-objective optimization of job applications redistribution in an online connection network system is addressed by incorporating monetary value of job applications as a signal into a ranker for ranking jobs with respect to a member profile in job search and recommendations. The monetary value of job applications is used in addition to the relevance signal and is determined by executing a machine learning model that accounts for the covariates that could impact monetary value of an application for a job.