MEMBER TO JOB POSTING SCORE CALCULATION

• US 20170032322A1
• Filed: 07/30/2015
• Published: 02/02/2017
• Est. Priority Date: 07/30/2015
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: LinkedIn Corporation (Microsoft Corporation)
• Inventors: Grover, Aman, You, Siyu, Kenthapadi, Krishnaram, Jain, Parul, Borisyuk, Fedor Vladimirovich, Degiere, Christopher Matthew, Guo, Songtao
• Application Number:
  US14/814,397
• Publication Number:
  US 20170032322A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/1053 Employment or hiring
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

In an example embodiment, a method is provided to calculate a distance for a first member of a social networking service and a job opening, which indicates the likelihood that the first member will obtain the job. The distance is calculated by applying one or more policies to member profiles of members who applied for similar jobs to obtain a hypothetical perfect candidate for the job. The member'"'"'s profile is then automatically compared to the hypothetical perfect candidate profile to calculate the distance.

Claims

1. A computer-implemented method comprising:

receiving metadata pertaining to job listing data corresponding to a job opening, the metadata including one or more requirements for the job opening;

obtaining a member profile for a first member of a social networking service;

obtaining a plurality of member profiles for members of the social networking service other than the first member, the plurality of member profiles corresponding to members who have applied for and obtained job offers for jobs similar to the job opening;

determining, for each of the plurality of member profiles, a date on which the corresponding member applied for the job similar to the job opening;

obtaining, for each of the plurality of member profiles, a version of the corresponding member profile as it was on the date on which the corresponding member applied for the job similar to the job opening, each of the versions of the corresponding member profiles including a vector of features of the corresponding member;

applying one or more policies to the vectors for the plurality of member profiles to obtain a vector of features representing a perfect candidate for the job opening;

obtaining a vector of features of the first member from the member profile for the first member; and

calculating a distance for the first member with respect to the perfect candidate for the job opening by comparing the vector of features representing the perfect candidate and the vector of features of the first member.

2. The computer-implemented method of claim 1, wherein the obtaining a plurality of member profiles includes:

receiving information about successful candidates for jobs similar to the job opening; and

comparing the information about successful candidates to member profiles in a member database to identify member profiles corresponding to the successful candidates.

3. The computer-implemented method of claim 2, wherein the information about successful candidates is contained in a successful candidates list stored in an internal database.

4. The computer-implemented method of claim 1, wherein the calculating includes:

calculating a similarity score for the first member with respect to the perfect candidate for the job opening by performing a similarity algorithm on the vector of features representing the perfect candidate and the vector of features of the first member; and

calibrating the similarity score by adjusting the similarity score based on input from one or more calibration components.

5. The computer-implemented method of claim 4, wherein the one or more calibration components includes a company selectivity component that analyzes current metrics on how selective the company associated with the job is and outputs a metric representing company selectivity.

6. The computer-implemented method of claim 4, wherein the similarity algorithm is a cosine similarity algorithm.

7. The computer-implemented method of claim 4, wherein the similarity algorithm is a Jaccard similarity coefficient algorithm.

8. A system comprising:

a member database storing member profiles of members of a social networking service;

a computer-readable medium having instructions stored thereon, which, when executed by a processor of a negative difference applier, cause the system to obtain, for each of a plurality of member profiles corresponding to successful candidates for a job similar to a job opening, a version of the corresponding member profile as it was on a date on which the corresponding member applied for the job similar to the job opening, each version of the corresponding member profiles including a vector of features of the corresponding member;

a computer-readable medium having instructions stored thereon, which, when executed by a processor of a perfect candidates generator, cause the system to

apply one or more policies to the vectors for the plurality of member profiles to obtain a vector of features representing a perfect candidate for the job opening;

obtain a vector of features of a first member from a member profile for the first member; and

calculate a distance for the first member with respect to the perfect candidate for the job opening by comparing the vector of features representing the perfect candidate and the vector of features of the first member.

9. The system of claim 8, wherein the perfect candidates generator includes:

a similarity score generator to generate a similarity score between the vector of features representing the perfect candidate and the vector of features of the first member;

a plurality of calibration components, each of the plurality of calibration components generating a metric related to a statistical likelihood that the first member will obtain a job offer for the job opening; and

a distance calibrator configured to calibrate the similarity score based on the metrics from the plurality of calibration components.

10. The system of claim 9, wherein the plurality of calibration components includes a profile strength component to analyze overall strength of the member profile of the first member based on how active the first member is in updating the member profile of the first member.

11. The system of claim 9, wherein the plurality of calibration components includes a market analyzer to analyze company sentiment regarding a company corresponding to the job opening.

12. The system of claim 9, wherein the plurality of calibration components includes a conversion-to-target (CTT) analyzer to determine a conversion rate of people having a position similar to the job opening at a different company.

13. The system of claim 9, wherein the plurality of calibration components includes an alumni network analyzer to analyze a number of alumni from institutions which the first member has attended who have obtained jobs at the company corresponding to the job opening.

14. The system of claim 9, wherein the plurality of calibration components includes a conversion rate analyzer to analyze the conversion rate of the first member with respect to prior jobs applied for.

15. The system of claim 9, wherein the plurality of calibration components includes a competition ranker that analyzes member profiles of other members also applying for the job opening and compares them to the member profile of the first member.

16. A non-transitory machine-readable storage medium having instruction data to cause a machine to perform the following operations:

receiving metadata pertaining to job listing data corresponding to a job opening, the metadata including one or more requirements for the job opening;

obtaining a member profile for a first member of a social networking service;

obtaining a plurality of member profiles for members of the social networking service other than the first member, the plurality of member profiles corresponding to members who have applied for and obtained job offers for jobs similar to the job opening;

determining, for each of the plurality of member profiles, a date on which the corresponding member applied for the job similar to the job opening;

obtaining, for each of the plurality of member profiles, a version of the corresponding member profile as it was on the date on which the corresponding member applied for the job similar to the job opening, each of the versions of the corresponding member profiles including a vector of features of the corresponding member;

applying one or more policies to the vectors for the plurality of member profiles to obtain a vector of features representing a perfect candidate for the job opening;

obtaining a vector of features of the first member from the member profile for the first member; and

calculating a distance for the first member with respect to the perfect candidate for the job opening by comparing the vector of features representing the perfect candidate and the vector of features of the first member.

17. The non-transitory machine-readable storage medium of claim 16, wherein the obtaining a plurality of member profiles includes:

receiving information about successful candidates for jobs similar to the job opening; and

comparing the information about successful candidates to member profiles in a member database to identify member profiles corresponding to the successful candidates.

18. The non-transitory machine-readable storage medium of claim 17, wherein the information about successful candidates is contained in a successful candidates list stored in an internal database.

19. The non-transitory machine-readable storage medium of claim 16, wherein the calculating includes:

calculating a similarity score for the first member with respect to the perfect candidate for the job opening by performing a similarity algorithm to the vector of features representing the perfect candidate and the vector of features of the first member; and

calibrating the similarity score by adjusting the similarity score based on input from one or more calibration components.

20. The non-transitory machine-readable storage medium of claim 19, wherein the one or more calibration components includes a company selectivity component that analyzes current metrics on how selective a company associated with the job is and outputs a metric representing company selectivity.