SYSTEMS AND METHODS FOR DETERMINING RECRUITING INTENT

• US 20150112765A1
• Filed: 10/22/2013
• Published: 04/23/2015
• Est. Priority Date: 10/22/2013
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Linkedln Corporation
• Inventors: Sundaresh, Suman, Hill, Andrew P., Bhasin, Anmol, Kumar, Deepak
• Application Number:
  US14/060,216
• Publication Number:
  US 20150112765A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/1053 Employment or hiring
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Techniques for identifying members of a social network service that exhibit recruiting intent are described. According to various embodiments, a set of members of an online social network service that self-identify as recruiters may be identified. The set of members that self-identify as recruiters may then be clustered into a group of engaged recruiters and a second group of non-engaged recruiters, and the group of engaged recruiters may be categorized as members exhibiting recruiting intent. Behavioral log data associated with the members exhibiting recruiting intent may then be accessed and classified as recruiting intent signature data. Thereafter, prediction modeling may be performed based on the recruiting intent signature data and a prediction model, to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data.

Claims

1. A computer-implemented method comprising:

identifying a set of members of an online social network service that self-identify as recruiters;

clustering the set of members that self-identify as recruiters into a group of engaged recruiters and a second group of non-engaged recruiters;

categorizing the group of engaged recruiters as members exhibiting recruiting intent;

accessing behavioral log data associated with the members exhibiting recruiting intent, and classifying the behavioral log data as recruiting intent signature data; and

performing prediction modeling, by a machine including a memory and at least one processor, based on the recruiting intent signature data and a prediction model, to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data.

2. The method of claim 1, wherein the identifying comprises:

accessing member profile data of members of the online social network service; and

identifying the set of members as being associated with member profile data that includes recruiter attributes.

3. The method of claim 2, wherein the recruiter attributes include at least one of a recruiter-focused experience position, a recruiter-focused employer, a recruiter-focused education position, a recruiter-focused academic institution, a recruiter-focused skill, and a recruiter-focused endorsement.

4. The method of claim 2, wherein the clustering further comprises:

analyzing interactions by each member in the set with a plurality of products of the online social network service; and

separating the set of members into the group of engaged recruiters and the second group of non-engaged recruiters, based on the analyzed interactions.

5. The method of claim 1, wherein the clustering further comprises validating the clustering, based on determining that one or more engagement metrics associated with the group of engaged recruiters indicates a greater degree of engagement with the online social network service in comparison to one or more engagement metrics associated with the group of non-engaged recruiters.

6. The method of claim 5, wherein the engagement metric includes a measure of a number of days of active use of the online social network service during a specific time period.

7. The method of claim 1, wherein the categorizing further comprises:

validating the categorization of the group of engaged recruiters as members exhibiting recruiting intent, based on determining that indicators of recruiting intent are overrepresented in the group of engaged recruiters.

8. The method of claim 7, wherein the indicators include at least one of a number of jobs posted, a number of career mail messages transmitted, and a subscription to a talent-finder service.

9. The method of claim 1, wherein the performing of the prediction modeling further comprises:

classifying the recruiting intent signature data associated with the members exhibiting recruiting intent as positive training samples for training the prediction model.

10. The method of claim 9, wherein the performing of the prediction modeling further comprises:

encoding the positive training samples into feature vectors; and

.

performing a training operation to refine coefficients of a logistic regression model, based on the feature vectors.

11. The method of claim 1, wherein the performing of the prediction modeling further comprises:

classifying behavior signal data associated with the group of non-engaged recruiters as negative training samples for training the prediction model.

12. The method of claim 11, wherein the performing of the prediction modeling further comprises:

classifying behavior signal data associated with a random selection of members of the online social network service that do not self-identify as recruiters and that do not exhibit indicators of recruiting intent as additional negative training samples for training the prediction model.

13. The method of claim 12, wherein the performing of the prediction modeling further comprises:

encoding the negative training samples into feature vectors; and

.

performing a training operation to refine coefficients of a logistic regression model, based on the feature vectors.

14. The method of claim 1, wherein the prediction model is any one of a logistic regression model, a Naï

ve Bayes model, a support vector machines (SVM) model, a decision trees model, and a neural network model.

15. The method of claim 1, further comprising:

assigning a recruiting intent score to each of the members of the online social network service, based on a degree of the match between the behavioral log data of the corresponding member and the recruiting intent signature data.

16. The method of claim 15, further comprising:

classifying members of the online social network service having recruiting intent scores greater than a specific threshold as members exhibiting recruiting intent; and

providing recruiter-focused recommendations to the members exhibiting recruiting intent.

17. The method of claim 16, wherein the recruiter-focused recommendations include recommendations for job candidates, recruiter-focused subscription offers, recruiter-focused articles, recruiter-focused advertisements, recruiter-focused member connections, and recruiter-focused group memberships.

18. The method of claim 15, further comprising:

classifying members of the online social network service having recruiting intent scores less than a specific threshold as members not exhibiting recruiting intent; and

preventing recruiter-focused recommendations from being provided to the members not exhibiting recruiting intent.

19. A system comprising:

a machine including a memory and at least one processor;

an identification module, executable by the machine, configured to;

identify a set of members of an online social network service that self-identify as recruiters;

cluster the set of members that self-identify as recruiters into a group of engaged recruiters and a second group of non-engaged recruiters; and

categorize the group of engaged recruiters as members exhibiting recruiting intent; and

a prediction module configured to;

access behavioral log data associated with the members exhibiting recruiting intent, and classifying the behavioral log data as recruiting intent signature data; and

perform prediction modeling based on the recruiting intent signature data and a prediction model, to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data.

20. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

identifying a set of members of an online social network service that self-identify as recruiters;

clustering the set of members that self-identify as recruiters into a group of engaged recruiters and a second group of non-engaged recruiters;

categorizing the group of engaged recruiters as members exhibiting recruiting intent;

accessing behavioral log data associated with the members exhibiting recruiting intent, and classifying the behavioral log data as recruiting intent signature data; and

performing prediction modeling based on the recruiting intent signature data and a prediction model, to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data.