MATCHING DEVICE, MATCHING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING PROGRAM

• US 20150248650A1
• Filed: 02/25/2015
• Published: 09/03/2015
• Est. Priority Date: 02/28/2014
• Status: Active Application

• Current Assignee: NEC Corporation
• Original Assignee: NEC Corporation
• Inventors: Nakamura, Nobutatsu, Tomonaga, Yasuyuki
• Application Number:
  US14/630,762
• Publication Number:
  US 20150248650A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/1053 Employment or hiring

Abstract

Provided are a matching device, a matching method, and a non-transitory computer readable medium storing a program, which are capable of performing matching between two pieces of information having different properties. A matching device that performs matching between a human resource that searches for an organization that meets a specific requirement and an organization that searches for a human resource that meets a specific requirement includes: a storage unit that holds human resource information indicative of a requirement for the human resource and organization information indicative of a requirement for the organization; and a matching processing unit that calculates a matching score representing a relationship between the human resource information and the organization information.

Claims

1. A matching device that performs matching between a human resource that searches for an organization that meets a specific requirement and an organization that searches for a human resource that meets a specific requirement, the matching device comprising:

a storage unit that holds human resource information indicative of a requirement for the human resource and organization information indicative of a requirement for the organization; and

a matching processing unit that calculates a matching score representing a relationship between the human resource information and the organization information.

2. The matching device according to claim 1, wherein

the matching processing unit comprises:

a pre-processing unit that generates a human resource vector and an organization vector based on the human resource information and the organization information, respectively;

a feature extraction unit that extracts features of the human resource vector and the organization vector and converts the extracted features into a human resource feature vector and an organization feature vector, respectively, the human resource feature vector and the organization feature vector being vectors of the same dimension; and

a matching score calculation unit that calculates, as the matching score, a similarity between the human resource feature vector and the organization feature vector.

3. The matching device according to claim 2, wherein the feature extraction unit extracts features of the human resource vector and the organization vector by using different hierarchical neural networks, and converts the extracted features into the human resource feature vector and the organization feature vector, respectively, the human resource feature vector and the organization feature vector being vectors of the same dimension.

4. The matching device according to claim 3, further comprising a matching learning unit that receives an instruction signal representing a relationship between the human resource and the organization, and optimizes the feature extraction unit based on a difference between the similarity and the instruction signal.

5. The matching device according to claim 4, wherein the matching learning unit adjusts learning parameters of the neural networks by error backpropagation using the difference.

6. The matching device according to claim 2, wherein the human resource vector and the organization vector are vectors of different dimensions that cannot be expressed in the same feature vector space.

7. The matching device according to claim 1, wherein at least one of the human resource information and the organization information is semi-structured data including structured numeric data and non-structured text data.

8. The matching device according to claim 2, wherein the matching processing unit calculates a matching score by calculating one of a cosine similarity and a norm between the human resource feature vector and the organization feature vector.

9. The matching device according to claim 1, further comprising a matching display unit that displays a matching result, wherein

the matching processing unit calculates matching scores between a specific organization and a plurality of human resources, and

the matching display unit arranges and displays the plurality of human resources in an ascending order or a descending order of the matching scores.

10. The matching device according to claim 1, further comprising a matching display unit that displays a matching result, wherein

the matching processing unit calculates matching scores between a specific human resource and a plurality of organizations, and

the matching display unit arranges and displays the plurality of organizations in an ascending order or a descending order of the matching scores.

11. The matching device according to claim 1, further comprising a matching display unit that displays a matching result, wherein

the matching processing unit calculates matching scores between an organization group including a plurality of organizations and a plurality of human resources, and

the matching display unit arranges and displays the plurality of human resources in an ascending order or a descending order of the matching scores.

12. A matching method that performs matching between a human resource that searches for an organization that meets a specific requirement and an organization that searches for a human resource that meets a specific requirement, the matching method comprising:

a matching processing step of calculating a matching score representing a relationship between human resource information indicative of a requirement for the human resource and organization information representing a requirement for the organization.

13. The matching method according to claim 12, wherein

the matching processing step comprises:

a pre-processing step of generating a human resource vector and an organization vector based on the human resource information and the organization information, respectively;

a feature extraction step of extracting features of the human resource vector and the organization vector and converting the extracted features into a human resource feature vector and an organization feature vector, respectively, the human resource feature vector and the organization feature vector being vectors of the same dimension; and

a matching score calculation step of calculating, as the matching score, a similarity between the human resource feature vector and the organization feature vector.

14. The matching method according to claim 13, wherein the feature extraction step includes extracting features of the human resource vector and the organization vector by using different hierarchical neural networks, and converting the extracted features into the human resource feature vector and the organization feature vector, respectively, the human resource feature vector and the organization feature vector being vectors of the same dimension.

15. The matching method according to claim 14, further comprising a matching learning step of receiving an instruction signal representing a relationship between the human resource and the organization, and optimizing the feature extraction step based on a difference between the similarity and the instruction signal.

16. The matching method according to claim 15, wherein the matching learning step includes adjusting learning parameters of the neural networks by error backpropagation using the difference.

17. The matching method according to claim 13, wherein the human resource vector and the organization vector are vectors of different dimensions that cannot be expressed in the same feature vector space.

18. The matching method according to claim 12, wherein at least one of the human resource information and the organization information is semi-structured data including structured numeric data and non-structured text data.

19. The matching method according to claim 13, wherein the matching processing step includes calculating a matching score by calculating one of a cosine similarity and a norm between the human resource feature vector and the organization feature vector.

20. The matching method according to claim 12, further comprising a matching display step of calculating matching scores between a specific organization and a plurality of human resources, and arranging and displaying the plurality of human resources in an ascending order or a descending order of the matching scores.

21. The matching method according to claim 12, further comprising a matching display step of calculating matching scores between a specific human resource and a plurality of organizations, and arranging and displaying the plurality of organizations in an ascending order or a descending order of the matching scores.

22. The matching method according to claim 12, further comprising a matching display step of calculating matching scores between an organization group including a plurality of organizations and a plurality of human resources, and arranging and displaying the plurality of human resources in an ascending order or a descending order of the matching scores.

23. A non-transitory computer readable medium storing a program for causing a computer to execute the matching method according to claim 12.