SYSTEM AND METHOD FOR GENERATING EMPLOYMENT CANDIDATES

• US 20200134537A1
• Filed: 01/29/2019
• Published: 04/30/2020
• Est. Priority Date: 10/30/2018
• Status: Active Application

• Current Assignee: Ascendify Corporation
• Original Assignee: Ascendify Corporation
• Inventors: Beason, William Alexander, Hendrickson, Matthew Leonard, Ball, Jason Earl
• Application Number:
  US16/260,822
• Publication Number:
  US 20200134537A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/358 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/953 Querying, e.g. by the use of web search engines
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/063112 Skill-based matching of a person or a group to a task

Abstract

Systems for determining alternative job titles to use when querying online recruiting databases. Multiple computers are operatively interconnected for determining a set of alternative job titles from a set of job parameters, and using the alternative job titles when querying online job and/or candidate posting corpora. A method embodiment commences upon parsing a given job requisition to identify at least one job title string that corresponds to at least one job title. The job title is normalized in accordance with a set of normalization rules to form a normalized job title string. The normalized job title string is then used to access machine learning models, which are in turn used to classify the job title string and to produce alternative titles that correspond to the job title. The alternative titles are used for querying online job posting corpora to retrieve postings that match at least one of the alternative titles.

Claims

1. A computer-implemented method for determining a set of alternative job titles, the method comprising:

processing a set of job parameters to identify at least one job title string that corresponds to at least one job title;

normalizing the at least one job title string in accordance with a set of normalization rules to form a normalized job title string;

querying a database of machine learning models to retrieve at least one alternative job title based at least in part on the normalized job title string; and

querying at least one online corpus to retrieve at least one entry that matches the at least one alternative job title.

2. The method of claim 1, wherein:

the querying of the database of machine learning models uses a first query language; and

the querying of the at least one online corpus uses a second query language.

3. The method of claim 1, wherein the machine learning models comprise one or more classifiers.

4. The method of claim 3, wherein a first classifier is trained based at least in part on job titles contained in seed data, and wherein a second classifier is trained based at least in part on job descriptions contained in the seed data.

5. The method of claim 1, wherein at least a portion of the set of job parameters is based on a job requisition.

6. The method of claim 1, wherein the at least one alternative job title is generated based on at least a partial match between at least a first portion of the at least one job title string and at least a second portion of a job title stored in a data structure of one or more of the machine learning models.

7. The method of claim 6, wherein the partial match is based at least in part on matching a number of Ngrams.

8. The method of claim 7, wherein at least one of the Ngrams comprises a syllable or a word stem.

9. The method of claim 6, wherein the partial match is based at least in part on a correlation between the first portion of the at least one job title string and a number of features of the database of machine learning models.

10. The method of claim 6, further comprising adding newly-encountered Ngrams to the database of machine learning models.

11. A system for determining a set of alternative job titles, the system comprising:

one or more processors; and

a memory storing instructions that, when executed by the one or more processors, cause the system to;

process a set of job parameters to identify at least one job title string that corresponds to at least one job title;

normalize the at least one job title string in accordance with a set of normalization rules to form a normalized job title string;

query a database of machine learning models to retrieve at least one alternative job title based at least in part on the normalized job title string; and

query at least one online corpus to retrieve at least one entry that matches the at least one alternative job title.

12. The system of claim 11, wherein execution of the instructions further causes the system to:

query the database of machine learning models using a first query language; and

query the at least one online corpus using a second query language.

13. The system of claim 11, wherein the machine learning models comprise one or more classifiers.

14. The system of claim 13, wherein a first classifier is trained based at least in part on job titles contained in seed data, and wherein a second classifier is trained based at least in part on job descriptions contained in the seed data.

15. The system of claim 11, wherein at least a portion of the set of job parameters are derived from a job requisition.

16. The system of claim 11, wherein the at least one alternative job title is generated based on at least a partial match between at least a first portion of the at least one job title string and at least a second portion of a job title stored in a data structure of one or more of the machine learning models.

17. The system of claim 16, wherein the partial match is based at least in part on matching a number of Ngrams.

18. The system of claim 17, wherein at least one of the Ngrams comprises a syllable or a word stem.

19. A non-transitory computer readable medium comprising instructions that, when executed by one or more processors of a device, causes the device to perform operations comprising:

processing a set of job parameters to identify at least one job title string that corresponds to at least one job title;

normalizing the at least one job title string in accordance with a set of normalization rules to form a normalized job title string;

querying a database of machine learning models to retrieve at least one alternative job title based at least in part on the normalized job title string; and

querying at least one online corpus to retrieve at least one entry that matches the least one alternative job title.

20. The non-transitory computer readable medium of claim 19, wherein:

querying the database of machine learning models uses a first query language; and

querying the at least one online corpus uses a second query language.