Job applicant probability of confirmed hire

Patent metadata

Field	Value
Publication number	US-2019034883-A1
Application number	US-201715660779-A
Country	US
Kind code	A1
Filing date	Jul 26, 2017
Priority date	Jul 26, 2017
Publication date	Jan 31, 2019
Grant date	—

Abstract

Techniques for predicting relevance of social networking service member accounts to a job posting. In an embodiment, a candidate predictor engine of a system encodes data representing an applicant quality (AQ) score for each job/applicant pair for a plurality of applicants to a job posting. Additionally, the system stores the encoded data and assigns member-level weights to each of the applicants. Moreover, the system calculates weighted AQ scores for each of the job/applicant pairs, the weighted AQ scores being products of respective AQ scores and member-level weights. Furthermore, the system sums the weighted AQ scores to derive a total weighted score for the job posting. Then, the candidate predictor engine generates a job-level probability of confirmed hire (pCH) based on the total weighted score, the job-level pCH indicating a likelihood of the posting being filled by an applicant. Also, the system transmits the job-level pCH to a client for display.

First claim

What is claimed is:

1 . A computer system, comprising:
a processor; a storage device; a candidate predictor engine; and a memory device holding an instruction set executable on the processor to cause the computer system to perform operations comprising:
encoding, by the candidate predictor engine, data representing an applicant quality (AQ) score for each job/applicant pair for a plurality of applicants to a job posting, the AQ score being based on an AQ model;
storing, on the storage device, the encoded data representing the AQ scores for each job/applicant pair for the plurality of applicants to the job posting;
assigning member-level weights to each of the plurality of applicants;
calculating, by the candidate predictor engine, weighted AQ scores for each of the job/applicant pairs, the weighted AQ scores being products of respective AQ scores retrieved from the storage device and member-level weights;
summing the weighted AQ scores to derive a total weighted score for the job posting;
generating, by the candidate predictor engine, a job-level probability of confirmed hire (pCH) based on the total weighted score, the job-level pCH indicating a likelihood of the job posting being filled by an applicant; and
transmitting the job-level pCH to a client device for display.

2 . The computer system of claim 1 , wherein assigning member-level weights to each of the plurality of applicants comprises:
retrieving, from the storage device, the respective AQ scores of the plurality of applicants; and assigning greater weights to applicants with higher AQ scores.

3 . The computer system of claim 1 , wherein the AQ scores are based in part on at least one pre-defined type of candidate feature retrieved from a job candidate context feature set stored on the storage device, the candidate feature comprising at least one of a job function descriptor represented in a social network service profile of a target candidate account, a job industry descriptor represented in the social network service profile of the target candidate account, and a job company size descriptor represented in the social network service profile of the target candidate account.

4 . The computer system of claim 1 , wherein the candidate prediction engine comprises a prediction generator, and wherein generating the job-level pCH further comprises generating, by the prediction generator, a prediction output based on job-level characteristics including one or more of a channel, an application path, and an organization size, the prediction output indicating whether applicants of the plurality of applicants are qualified for the job posting.

5 . The computer system of claim 4 , wherein the job-level characteristics include at least one pre-defined type of job feature comprising at least one of a job function descriptor represented in the job posting, a job industry descriptor represented in the job posting, and a job company size descriptor represented in the job posting.

6 . The computer system of claim 3 , wherein encoding the data representing the AQ score for each job/applicant pair comprises encoding a candidate-to-job comparison feature data subset to include a difference value that corresponds with a pre-defined type of comparison feature that is learned as being predictive of whether a given job candidate is qualified for a given job posting, encoding the candidate-to-job comparison feature data subset comprising:
identifying years of candidate professional experience based on at least one employment time period descriptor in a social network service profile of the target candidate account; identifying years of required professional experience described in the job posting; calculating a years difference value to represent a difference between the years of candidate professional experience and the years of required professional experience; inserting the years difference value into the candidate-to-job comparison feature data subset as a respective comparison feature; and storing the candidate-to-job comparison feature data subset on the storage device.

7 . The computer system of claim 6 , wherein inserting the years difference value into the candidate-to-job comparison feature data subset as the respective comparison feature comprises:
encoding the years difference value into a pre-defined data position for a years difference comparison feature in the candidate-to-job comparison feature data subset; and storing the candidate-to-job comparison feature data subset on the storage device.

8 . The computer system of claim 3 , wherein encoding the data representing the AQ score for each job/applicant pair comprises encoding a candidate-to-job comparison feature data subset to include a difference value that corresponds with a pre-defined type of comparison feature that is learned as being predictive of whether a given job candidate is qualified for a given job posting, encoding the candidate-to-job comparison feature data subset comprising:
identifying each skill descriptor in a social network service profile of the target candidate account; identifying each skill descriptor described in the job posting; calculating a skill match value to represent a percentage of matching skill descriptors between the target candidate account's skill descriptors and the job posting's skill descriptors; inserting the skill match value into the candidate-to-job comparison feature data subset as a respective comparison feature; and storing the candidate-to-job comparison feature data subset on the storage device.

9 . The computer system of claim 8 , wherein each skill descriptor in the social network service profile of the target candidate account comprises a respective skill descriptor selected by the target candidate account.

10 . The computer system of claim 8 , wherein each skill descriptor in the social network service profile of the target candidate account comprises a respective skill descriptor selected by a member account having a social network connection with the target candidate account.

11 . The computer system of claim 8 , wherein inserting the skill match value into the candidate-to-job comparison feature data subset as a respective comparison feature comprises:
encoding the skill match value into a pre-defined data position for a skill match comparison feature in the candidate-to-job comparison feature data subset; and storing the candidate-to-job comparison feature data subset on the storage device.

12 . The computer system of claim 1 , wherein the operations further comprise instantiating a plurality of job candidate decision trees, each job candidate decision tree comprising at least one learned decision tree branch label, instantiating the plurality of job candidate decision trees comprising:
collecting, from within a social network service, logged interaction data comprising at least a first previous determination, by a first employer account, of a first candidate account as qualified for a first job posting, and a second previous determination, by a second employer account, of a second candidate account as not qualified for a second job posting.

13 . The computer system of claim 12 , wherein instantiating the plurality of job candidate decision trees, each job candidate decision tree comprising at least one learned decision tree branch label, further comprises:
executing a random forest process with the logged interaction data to build each job candidate decision tree by learning a branch label for each branch of each job candidate decision tree based on at least one attribute of the logged interaction data.

14 . A comput

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Liang Keqing
• Saxena Vibhu Prakash
• Phan Jason

Classifications

• G06Q10/40

  Business processes related to social networking or social networking services · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06Q10/1053 Primary

  Employment or hiring · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N5/04

  Inference or reasoning models · CPC title