This invention application presents a cyclic two-way bidding matching method and system used to periodically match resumes and recruitment positions by the way of bidding matching. Every round of bidding matching cycle comprises: comparing the pre-matching items of resumes and positions, and establishing a pre-matching relation between the positions and resumes if their pre-matching items match each other; based on the pre-matching relation, quantifying each resume's attraction and each position's attraction; building the bidding queue of resumes and the bidding queue of positions, then the two-way bidding matching is implemented to match resumes and positions to get the two-way optimal matching results. In every round of bidding matching cycle, a certain number of matching results are generated for each resume and for each position. As the bidding matching periodically runs rounds after rounds, more and more two-way optimal matching results are generated.

Systems and methods for visually representing a process graph are provided. A process graph representing execution of a process is received. One or more gateway nodes in the process graph are folded into their from-nodes based on a number of incoming edges and a number of outgoing edges of the one or more gateway nodes. The process graph according to the folded one or more gateway nodes is output.

Removing bias when matching job candidates to open positions by obtaining candidates’ data including information about the job candidates and a likelihood rate that the candidate matches the open position, identifying protected characteristics from the candidates’ data, generating a training data set that does not bias within groups of candidates having different protected characteristics, where the training data set includes a portion of the job candidates, training a model based on the training data set, applying the trained model on a test set, where the test set is different from the training data set, and determining a fairness measurement value of the trained model using the results of the model on the test set and protected characteristics of candidates of the test set.

Methods, systems, and computer programs are presented for presenting return-on-investment (ROI) information, for budgeted services that resulted in a successful service delivery, on a user interface for setting the budget for a service request. One method includes an operation for identifying daily budgets for budgeted services that resulted in a successful service delivery (BSSSD). Each daily budget indicates an amount for spending in promotion of the BSSSD in an online service. The method further includes receiving a request, in a graphical user interface (GUI) of the online service, for posting a daily budget for a first budgeted service. Further, a performance value, associated with the daily budgets of the BSSSD that are similar to the first budgeted service, is selected. Further, the method includes causing presentation, by the one or more processors, of the performance value in the GUI.

An employment recruitment method based on face recognition includes acquiring a candidate's data from a third-party website, analyzing the candidate's data by a semantic analysis method to identify human resources information of the candidate, and analyzing messages and postings in the human resources information of the candidate to determine candidate's personality. A terminal device acquires a second face image of the candidate by a second camera, analyzes the second face image of the candidate by a computer vision algorithm to determine a micro-expression of the candidate, and provides the candidate's human resources information, the candidate's personality, and the candidate's micro-expression to the recruiter to evaluate the candidate. The terminal device applying the method is also disclosed.

A system and method for managing usage rights, and autonomous creation of a usage rights and release agreement between a client and a talent for usage rights of job materials. The system includes an interface module for generating a client interface and a talent interface; a negotiation module for generating usage rights charge based on the price, the details of the client, and the details of the job; a contract module to generate a usage rights and release agreement based on the details of a talent, the usage rights charge, the details of the client, and the details of the job to; and generating a contract between the client and a talent based on the usage rights and release agreement.

Aspects relate to systems and methods for parsing and correlating solicitation video content. An exemplary system includes a computing device configured to receive a solicitation video related to a subject, where the solicitation video includes at least an image component and at least an audio component, where the audio component includes audible verbal content related to at least an attribute of the subject, transcribe at least a keyword as a function of the audio component, and associate the subject with at least a job description as a function of the at least a keyword.

A system for connecting a user to an employment resource tailored to the user includes: a computational device configured to assess the user, using an assessment questionnaire, regarding one or more of an interest, a hard skill, a soft skill, a job-specific skill, an occupational personality trait, training, education, experience, and aptitude for a job. A method for connecting a user to an employment resource tailored to the user includes: assessing, by a computational device, using an assessment questionnaire, the user regarding one or more of an interest, a hard skill, a soft skill, a job-specific skill, an occupational personality trait, training, education, experience, and aptitude for a job; and presenting, by the computational device, the assessment to one or more of the user and a prospective employer of the user.

A method includes receiving uncompilable code from a candidate. The method further includes extracting features from the uncompilable code. The method further includes outputting, with a coding machine-learning model, compilable code based on the uncompilable code and the extracted features. The method further includes generating a coding score based on the uncompilable code and the compilable code. The method further includes receiving first media of one or more answers to questions provided by the candidate during an interview. The method further includes outputting, with a media machine-learning model, one or more corresponding ratings for the one or more answers. The method further includes generating a media score based on the one or more corresponding ratings. The method further includes generating a total score based on the coding score and the media score.

A method includes receiving uncompilable code from a candidate. The method further includes extracting features from the uncompilable code. The method further includes outputting, with a coding machine-learning model, compilable code based on the uncompilable code and the extracted features. The method further includes generating a coding score based on the uncompilable code and the compilable code. The method further includes receiving first media of one or more answers to questions provided by the candidate during an interview. The method further includes outputting, with a media machine-learning model, one or more corresponding ratings for the one or more answers. The method further includes generating a media score based on the one or more corresponding ratings. The method further includes generating a total score based on the coding score and the media score.