CN112966966A - Talent introduction index control method for introduced talent matching - Google Patents
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Abstract
The invention relates to a talent introduction index control method for introduced talent matching, which specifically comprises the following steps: s1, obtaining a target introduction organization and a plurality of target introduction talents considering introduction; s2, respectively collecting objective index data of the target introduced talents and the target introduced organizations; s3, extracting introduction index key words from objective index data of target introduction talents and target introduction organizations, and calculating talent introduction indexes according to the extracted introduction index key words. Compared with the prior art, the method has the advantages of making up the defect that the quantitative matching evaluation index of objective elements is lacked in the prior art, effectively improving the accuracy of the matching result of the introduced talents and the like.
Description
Technical Field
The invention relates to the technical field of talent introduction evaluation, in particular to a talent introduction index control method for talent introduction matching.
Background
Talent movement refers to the movement of talents between organizations or the change from one working state to another, and the working state can be determined according to the job position, job site, professional nature, service object and its nature, etc. Talent flow is a basic form of talent regulation, and is an important link essential for adjusting the social structure of talents and fully playing the potential of talents. Human resources, an important resource in social production, must be orderly moved. Only when people flow, reasonable allocation of human resources can be realized, and the utilization rate of the human resources can be improved. With the development of economy and society, there are many ways to choose talents: there are network recruitment, newspaper and television, acquaintance introduction, professional brokers, etc. Factors influencing talent movement mainly include: the adjustment of industrial structure, the development of science and technology, the update of professionalism, the requirement of economic development, the competition condition of talents, the adjustment of talent structure and the like.
Talent flow is divided into reasonable and unreasonable, forward and reverse flows. Talent flow meeting the needs of socio-economic development may be referred to as rational, positive flow, and conversely, non-rational, reverse flow. Reasonable effectiveness of talent movement depends on the degree of match of the mobile talent with the introduction institution or subject. The high matching degree of the introduced talents of the institution or subject is beneficial to the development of the introduced talents and the development of the institution or subject.
At present, the introduced talent evaluation method mainly comprises talent flow evaluation based on matching between people and tissues and talent flow evaluation based on environment matching. Based on talent flow evaluation of matching between the human beings and the tissues, the influence of consistency matching and compensatory matching between the tissues and the talents on the flow of the talents is revealed through the analysis of the matching conditions between the talents and the tissues and the tendency of talent flow; talent flow evaluation based on environment matching analyzes the economic, cultural, atmosphere, value, target and standard of the environment according to a human-environment matching theory, and the influence of social life supply, finance, occupational development space, work tasks, interpersonal relationship of the environment and compensatory matching of talent requirements on talent flow.
The current matching evaluation mode of the introduced talents is basically qualitative evaluation and subjective evaluation, and quantitative matching evaluation indexes of objective elements are lacked.
Disclosure of Invention
The invention aims to overcome the defect of low accuracy of talent matching results caused by the lack of quantitative matching evaluation indexes of objective elements in the prior art and provide a talent introduction index control method for introduced talent matching.
The purpose of the invention can be realized by the following technical scheme:
a talent introduction index control method for talent introduction matching, comprising the steps of:
s1, obtaining a target introduction organization and a plurality of target introduction talents considering introduction;
s2, respectively collecting objective index data of the target introduced talents and the target introduced tissues;
s3, extracting introduction index key words from objective index data of target introduction talents and target introduction organizations, and calculating talent introduction indexes according to the extracted introduction index key words.
The types of the objective index data of the target introduction organization comprise scientific and technological policies, scientific and technological investment, startup fund, research results, scientific research projects, recruitment information and geographic information.
The types of the objective index data of the target introduced talents comprise activity tracks, job-undertaking institutions, national household registers, research results, scientific research projects, identity information and job-seeking requirements.
The objective index data is collected by searching through big data technology.
The target introduction organization is specifically a subject or an organization, and the corresponding types include colleges and universities, scientific research units and enterprises.
The types of targeted introduced talents include teachers, researchers, and corporate technology backbones.
The calculating of the talent introduction index in step S3 specifically includes the steps of:
s301, assigning the extracted introduction index keywords according to preset keyword scores;
s302, respectively calculating the weighted score of each introduction index keyword in the target introduction talents and the weighted score of each introduction index keyword in the target introduction organization according to the weight corresponding to the introduction index keywords;
s303, taking the introduction index key words of the target introduction talents as word sources and the introduction index key words of the target introduction organizations as contrast words, and calculating the similarity score of each word source to the contrast words;
s304, multiplying the weighted score and the similarity score of each word source and the comparison word, and summing according to the total number of the word sources to obtain talent introduction indexes of corresponding target introduced talents;
s305, ranking the talent introduction indexes among the target introduced talents to obtain the best introduced talents.
The value range of the weight corresponding to the introduction index keyword is 0-1.
And assigning the weight corresponding to the introduction index keyword according to the importance degree of the introduction index keyword.
The similarity score is calculated by a semantic similarity algorithm.
And the keyword score and the similarity score are natural numbers.
The talent introduction indices are sorted in the step S305 in descending order, and the target introduced talents corresponding to the first sorted order are taken as the best introduced talents.
Compared with the prior art, the invention has the following beneficial effects:
the invention respectively collects objective index data of a target introduced talent and a target introduced organization by applying a big data technology, classifies and extracts keywords, then weights the introduced index keywords in the objective index data according to the requirements of the introduced talents, calculates the similarity score between the target introduced talent and the introduced index keywords of the target introduced organization by applying an artificial intelligence semantic similarity calculation method, obtains a talent introduction index, accurately matches the introduced talents, overcomes the defect that the quantitative matching evaluation index of objective elements is lacked in the prior art, and effectively improves the accuracy of the matching result of the introduced talents.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 1, a talent introduction index control method for talent introduction matching specifically includes the following steps:
s1, obtaining a target introduction organization and a plurality of target introduction talents considering introduction;
s2, respectively collecting objective index data of the target introduced talents and the target introduced organizations;
s3, extracting introduction index key words from objective index data of target introduction talents and target introduction organizations, and calculating talent introduction indexes according to the extracted introduction index key words.
The types of objective index data of the target introduction organization comprise scientific and technological policies, scientific and technological investment, startup fund, research results, scientific research projects, recruitment information and geographic information.
The types of objective index data of the target introduced talents include activity tracks, job-undertaking institutions, national household registers, research results, scientific research projects, identity information and job-seeking requirements.
The objective index data is collected by searching through big data technology.
The target introduction organization is specifically a subject or an organization, and the corresponding types include colleges and universities, scientific research units and enterprises.
Types of targeted introduced talents include teachers, researchers, and corporate technology backbones.
The step of calculating the talent introduction index in step S3 specifically includes the steps of:
s301, assigning the extracted introduction index keywords according to preset keyword scores;
s302, respectively calculating the weighted score of each introduction index keyword in the target introduction talents and the weighted score of each introduction index keyword in the target introduction organization according to the weight corresponding to the introduction index keywords;
s303, taking the introduction index key words of the target introduction talents as word sources and the introduction index key words of the target introduction organizations as contrast words, and calculating the similarity score of each word source to the contrast words;
s304, multiplying the weighted score and the similarity score of each word source and the comparison word, and summing according to the total number of the word sources to obtain talent introduction indexes of corresponding target introduced talents;
s305, ranking the talent introduction indexes among the target introduced talents to obtain the best introduced talents.
The value range of the weight corresponding to the introduced index key words is 0-1.
And assigning the weight corresponding to the introduced index key words according to the importance degree of the introduced index key words.
In this embodiment, the number of the target introduced talents and the number of the introduction index keywords of the target introduction organization are different, the number of the introduction index keywords of the target introduction organization is 20, and the number of the introduction index keywords of the target introduced talents is 10.
The similarity score is specifically calculated by a semantic similarity algorithm.
The keyword score and the similarity score are natural numbers.
In this embodiment, the keyword score of each introduction index keyword of the target introduction talent is 100 points, the keyword score of each introduction index keyword of the target introduction organization is 100 points, and the numerical range of the similarity score is 0 to 100.
The talent introduction indices are sorted in descending order in step S305, and the target introduced talents corresponding to the first sorted order are taken as the best introduced talents.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (10)
1. A talent introduction index control method for talent introduction matching, comprising the steps of:
s1, obtaining a target introduction organization and a plurality of target introduction talents considering introduction;
s2, respectively collecting objective index data of the target introduced talents and the target introduced tissues;
s3, extracting introduction index key words from objective index data of target introduction talents and target introduction organizations, and calculating talent introduction indexes according to the extracted introduction index key words.
2. The method as claimed in claim 1, wherein the type of the objective index data of the target introduction organization includes a technological policy, a technological investment, an entrepreneurial fund, a research result, a scientific research project, recruitment information, and geographic information.
3. The method of claim 1, wherein the types of objective index data of the target introduced talent include an activity track, a job-giving institution, nationality household registration, a research result, a scientific research project, identity information, and job-seeking requirements.
4. The method as claimed in claim 1, wherein the target introduction organization is a subject or an organization, and the corresponding types include colleges, research units and enterprises.
5. The method of claim 1, wherein the type of the target introduced talent includes teacher, researcher and corporate technology backbone.
6. The method as claimed in claim 1, wherein the calculating of talent introduction index in step S3 comprises the steps of:
s301, assigning the extracted introduction index keywords according to preset keyword scores;
s302, respectively calculating the weighted score of each introduction index keyword in the target introduction talents and the weighted score of each introduction index keyword in the target introduction organization according to the weight corresponding to the introduction index keywords;
s303, taking the introduction index key words of the target introduction talents as word sources and the introduction index key words of the target introduction organizations as contrast words, and calculating the similarity score of each word source to the contrast words;
s304, multiplying the weighted score and the similarity score of each word source and the comparison word, and summing according to the total number of the word sources to obtain talent introduction indexes of corresponding target introduced talents;
s305, ranking the talent introduction indexes among the target introduced talents to obtain the best introduced talents.
7. The method as claimed in claim 6, wherein the weight value corresponding to the introduction index keyword ranges from 0 to 1.
8. The method as claimed in claim 6, wherein the similarity score is calculated by a semantic similarity algorithm.
9. The method of claim 6, wherein the keyword score and the similarity score are natural numbers.
10. The method as claimed in claim 6, wherein the talent introduction indices in step S305 are arranged in descending order, and the target introduced talent corresponding to the first order is selected as the best introduced talent.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113468418A (en) * | 2021-06-21 | 2021-10-01 | 广州政企互联科技有限公司 | Intelligent policy data recommendation method and system |
| CN113868423A (en) * | 2021-10-13 | 2021-12-31 | 上海市研发公共服务平台管理中心 | Talent introduction path selection method, device, storage medium and terminal |
| CN114971366A (en) * | 2022-06-14 | 2022-08-30 | 杭州市高层次人才发展服务中心 | Talent flow evaluation method based on regional analysis, storage medium and electronic device |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113468418A (en) * | 2021-06-21 | 2021-10-01 | 广州政企互联科技有限公司 | Intelligent policy data recommendation method and system |
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| CN114971366A (en) * | 2022-06-14 | 2022-08-30 | 杭州市高层次人才发展服务中心 | Talent flow evaluation method based on regional analysis, storage medium and electronic device |
| CN114971366B (en) * | 2022-06-14 | 2023-07-07 | 杭州市高层次人才发展服务中心 | Talent flow evaluation method based on area analysis, storage medium and electronic equipment |
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