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.

Abstract: In some embodiments, the disclosed subject matter involves a system and method relating to evaluating the quality of recommendations provided to a member of a recommendation service. Techniques and embodiments described herein may be applied to various recommendation services/engines. Middleware may be used to receive a member ID from a user and forward the member ID to a recommendation engine without the user having to log on as the member to access recommendations. Results of the recommendation engine may be received by the middleware and forwarded to the user for the selected member to be displayed proximate to the member profile. The user may annotate the recommendations with a relevancy rating to evaluate the quality of the recommendation engine and provide input to retrain recommendation models of the engine. Other embodiments are described and claimed.