US 8,818,910 B1 · Systems and methods for prioritizing job candidates using a decision-tree forest algorithm

Abstract

Computer-based systems and methods for prioritizing a list of job candidates for a job opening(s) with a hiring firm use the Random Forest algorithm, in which a number of decision trees are generated and the job candidates' respective probabilities of being the right fit for the hiring firm and for the position(s) are computed using the forest of decision trees. The job candidates may then be ranked according to their computed hiring probability. Non-conventional variables can be used, such as social media-related variables and external industry variables, such as the candidates' driving records. Other non-conventional variables that can be used include results from a personality or behavior assessment, results from a third-party skill assessment, and performance evaluations for prior projects or jobs of the candidates.

Claims18

Dependent claims

A system for prioritizing a list of job candidates for a job opening with a firm, the system comprising:

at least one computer system comprising at least one processor and at least one memory unit that is in communication with the at least one processor, wherein the at least one processor is programmed to:
use a Random Forest Algorithm to generate a quantity T decision trees, where T is greater than or equal to 2, and the T decision trees are trained based on a training data set of historical candidates classified into hired or unhired classes, wherein each decision tree of the T decision trees is generated by selecting historical candidates in the training dataset randomly with replacement to train the decision tree;
and
for each of the job candidates, determine a classification of the job candidate for each of the T decision trees;
compute a hiring probability for each job candidate based on a ratio of (i) a total of number of times the job candidate was classified as hired to (ii) T;
and
rank the job candidates by hiring probability to generate a prioritized list of the job candidates.

4 more elements

A computer-based method for prioritizing a list of job candidates for a job opening with a firm, the method comprising:

using a Random Forest Algorithm to generate, by a computer system that comprises at least one processor and at least one memory unit, a quantity T decision trees, where T is greater than or equal to 2, and the T decision trees are trained based on a training data set of historical candidates classified hired or unhired classes, wherein each decision tree of the T decision trees is generated by selecting historical candidates in the training dataset randomly with replacement to train the decision tree;
and
for each of the job candidates, determining, by the computer system, a classification of the job candidate for each of the T decision trees;
computing, by the computer system, a hiring probability for each job candidate based on a ratio of (i) a total of number of times the job candidate was classified as hired to (ii) T;
and
ranking, by the computer system, the job candidates by hiring probability to generate a prioritized list of the job candidates.

3 more elements

Dependencies are inferred from the claim text.

Systems and methods for prioritizing job candidates using a decision-tree forest algorithm

Life of this patent

Systems and methods for prioritizing job candidates using a decision-tree forest algorithm

Life of this patent

Abstract

Claims18