In some embodiments, a method can include receiving a set of job descriptions and a set of candidate profiles. Each job description is associated with a first subset of candidate profiles from the set of candidate profiles. The method can further include executing a model to identify, from the first subset of candidate profiles, a second subset of candidate profiles that satisfy a fit metric and a third subset of candidate profiles that does not satisfy the fit metric. The method can further include calculating a bias metric based on a true positive value, a false positive value, a true negative value, and a false negative value that were calculated based on auditing the second subset of candidate profiles and the third subset of candidate profiles. The method can further include updating the set of job descriptions based on the bias metric.

Techniques are described for receiving an online request to verify an asset, e.g., a line-item from an online profile. The technique determines a verifying entity, such as an educational institution, that has authority to verify the line-item, e.g., an earned certificate, and sends a request to such verifying entity to do so. The asset is added to and verified on the distributed ledger or is verified before adding to the distributed ledger. A verification indicator is coupled to the asset, signaling that the asset has been verified once and need not be verified again. A notification that the asset is verified is transmitted to interested parties. The verification may be at a level, such as a one day verification of a negative drug test. The verifications are searchable on the distributed ledger, e.g., an employer may query for verified assets that match job-related requirements.

An automated business method for matching education, salary and other employment-related data is disclosed. In one example embodiment, the automated business method includes integrating of two or more neural networks to match job-related services.

A system for scoring and standard analysis of user responses to an assessment test, wherein the system includes a scoring engine having one or more rubric items used to score and assess a candidate’s response to one or more free-text questions. A candidate’s response can be input into the scoring engine and optionally in communication with a machine learning classifier can produce one or more outputs. The outputs can include a score, recommendation, and user feedback among other things. The system can further include one or more machine learning classifier engines.

A system for scoring and standard analysis of user responses to an assessment test, wherein the system includes a scoring engine having one or more rubric items used to score and assess a candidate’s response to one or more free-text questions. A candidate’s response can be input into the scoring engine and optionally in communication with a machine learning classifier can produce one or more outputs. The outputs can include a score, recommendation, and user feedback among other things. The system can further include one or more machine learning classifier engines.

There is provided a method for cross-regional talent flow intention analysis, an electronic device, and a storage medium, which relates to technical fields such as big data processing and data statistics and analysis. A specific implementation solution involves: constructing a talent flow intention network based on search data in a network within a preset period of time; and performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result.

There is provided a method for cross-regional talent flow intention analysis, an electronic device, and a storage medium, which relates to technical fields such as big data processing and data statistics and analysis. A specific implementation solution involves: constructing a talent flow intention network based on search data in a network within a preset period of time; and performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result.

The present disclosure relates to an apparatus and a method for matching an employer to a job seeker based on a metaverse platform. According to the present disclosure, various experts are collected through an expert (user) pool, such that the knowledge and the experience are shared. The needs of the expert and the enterprise looking for the expert are satisfied through the expert platform. Information on the user is protected based on a blockchain technology.

The present disclosure relates to an apparatus and a method for matching an employer to a job seeker based on a metaverse platform. According to the present disclosure, various experts are collected through an expert (user) pool, such that the knowledge and the experience are shared. The needs of the expert and the enterprise looking for the expert are satisfied through the expert platform. Information on the user is protected based on a blockchain technology.

One example method includes receiving, at a decision tree trained with a group of training observations, a group of new observations, traversing the decision tree with the new observations, calculating, for one or more nodes of the decision tree, a respective local diversity score, and aggregating the local diversity scores to create an aggregate diversity score, and the aggregate diversity score indicates an extent to which one or more of the new observations are similar, in one or more respects, to the group of training observations.