# An Efficient Interpretable Visualization Method of Multidimensional Structural Data Matching Based on Job Seekers and Positions

Authors: Guoliang Si; Hengyi Lv; Hangfei Yuan; Dan Xie; Ce Peng
Journal: Discrete Dynamics in Nature and Society
 (2021)

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## Abstract

With the rapid development of Internet technology, millions of small, medium, and microenterprises are using Internet recruitment platforms to host their recruitment information. They have different job requirements and benefits positions. It is important to understand them for job seekers when choosing a position. Existing Internet recruitment platforms do not provide a detailed analysis of positions and visual methods for multidimensional matching of positions and job applicants. Candidates need to spend a lot of energy to screen out suitable positions. In this paper, we propose an efficient interpretable visualization method of multidimensional structural data matching based on job seekers and positions. First, we extract the keywords of the job seeker’s ability and benefits based on personal information, and we generate a job seeker ability table and a job seeker demand table. After that, we calculate the degree of the support, confidence, and promotion of each rule through the association rules generated by each frequent itemset of recruitment data to obtain the association rule table. We further explore the relationship between the skills required for the three types of positions based on the association rule. Finally, we use the regression method to build a salary forecasting model. On this basis, we predict the salary of job seekers based on the work experience, education, and work city provided by the job seeker. Simulation results show that our method has better performance on the job analysis and recommendation.


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## Body

## 1. Introduction

With the continuous development of society, the competition among enterprises has intensified, and the competition for talents among enterprises has become increasingly fierce. How to find the talents they need more effectively and quickly has become a difficult problem faced by human resource managers of various enterprises. Recruitment websites have gradually become the main way of applying for talents around the world due to their advantages such as large information capacity, low recruitment costs, and breaking time and space constraints [1]. With the development of big data, the data generated by recruitment websites have shown explosive growth. Massive data contain an inestimable value, which mainly includes the demand for market talents, the cultivation of college students, and the future development direction of various disciplines [2].To solve the above problems, many domestic and foreign scholars have actively explored and tried. By comparing the research work of various types of recruitment data analysis, from the perspective of recruitment data analysis, existing work can be divided into the following three categories:(1)
Analyze based on the information of job applicants on the recruitment website. Daniel et al. [3] focused on the study of the relationship between the fit of job seekers using the recruitment website and the satisfaction of the website and their opinions on the organization. They established a theoretical model by using 199 questionnaire surveys and structural equation models based on the recruitment website. The final results show that the content of the website, the job applicant’s understanding of the organization, and the job applicant’s social network’s recognition of the organization and work in a meaningful way knowing has a positive impact. Zhang and Zheng [4] used text mining techniques to observe the degree of gender differences in expected wages and job-related self-perceptions from recruitment websites, which triggered that the differences in global women’s self-perceptions may be related to the belief change theory. They also put forward self-views to explain the expected salary. Wadhawan and Sinha [5] study and identify the factors affecting online job search among students of Delhi from state and private universities using four statistical tools. The final result identified various factors affecting the perception of millennials and postmillennial towards online recruitment and the significant difference among job seekers of different age groups towards different factors. Priyadarshini et al. [6] discussed the influence dimensions, mechanisms, and conditions of the information characteristics of corporate recruitment websites on job seekers’ searching and recommendation intentions. Using the stimulus-organism-response framework, the data of 181 job applicants were analyzed. The final result showed that the influence of information characteristics such as information relevance, information accuracy, and information timeliness was determined by the attitude-mediated direct creation of job search intentions and recommendation intentions. These works focus on the analysis and integration of job seekers’ information on recruitment websites, ignoring the important impact of enterprise information on data matching.(2)
Analyze based on corporate information on recruitment websites. Eger et al. [7] found that, according to a survey on recruitment websites, respondents who followed the recruitment information on the company’s social media profile did not consider the company’s personal information on social networking sites, they believe that the organization’s personal information on social networking sites is very important. Baum and Kabst [8] compared the influence of recruitment companies and recruitment websites on the attractiveness of job seekers through research and explained how recruitment activities interact. The final result shows that the influence of companies on the attractiveness of job applicants on the website is significantly stronger than that of print ads. Walker et al. [9] studied the possibility that the diverse clues of companies on the recruitment website affect the processing of the presented information by the website visitors. Using a controlled experiment and a hypothetical organization, it was finally found that, after taking into account the perceived organizational attributes and website design effects, black and white participants had similar relationships when browsing real organizations and recruiting companies. These works are analyzed based on corporate information on recruitment websites. Although the company’s job advertisements have an indirect impact on the attractiveness of job seekers which is mediated by the employer’s knowledge, they ignore the impact of multiple recruitment activities on the accuracy of job seekers’ personal recommendation results.(3)
Visual analysis based on recruitment website data. Zhang and Wei [10] and others obtained data-related job recruitment information from different recruitment websites, constructed data-type job characteristics lexicon, studied the characteristics of da-ta-type job talent requirements, and visualized the characteristics and obtained the educational and professional requirements of different positions and differences in professional knowledge and similarities in ability and work experience. Kuhn and Shen [11] conducted research and visual analysis of recruitment data for artificial intelligence at home and abroad. The final study showed that the use of big data measurement methods to speculate on the skill requirements of college graduates by enterprises can reveal the era of artificial intelligence. The content and structure of skills that college graduates need to possess provide enlightenment for colleges and universities to deepen teaching reform. Sodhi and Son [12] crawled professional recruitment data of operations research from recruitment websites, then created a skill dictionary and a keyword dictionary, and used the methods of frequency statistics and content analysis to study the various skills of operations research in different positions. There are differences in demand. The above methods only analyzed the information of job seekers or enterprises from the perspective of visualization and did not give efficient strategies to achieve accurate matching between enterprises positions and job seekers.Different from the above methods, this article proposes a visual method of multidimensional matching of job applicants and positions, through detailed analysis of job skills requirements, occupational salary, and geographic location, and displays them in a visual manner and through job applicants. The information provided is matched with the characteristics of the recruitment position from multiple angles, and the matching results and job applicant ability analysis report are given from both the ability and demand. Finally, the above method is verified through examples. The main contributions of this paper are shown as follows:(1)
We extract the keywords of the job seeker’s ability and benefits based on personal information, and we generate a job seeker ability table and a job seeker demand table. Base on that, we calculate the degree of the support, confidence, and promotion of each rule through the association rules generated by each frequent itemset of recruitment data to obtain the association rule table.(2)
We realize the detailed analysis of job skills requirements, occupational salary, and geographic location, and we display them in a visual manner. We use the information provided by job applicants to match the characteristics of the job position from multiple perspectives and determine the matching results and job applicant ability analysis reports from both capabilities and needs.(3)
We use the regression method to build a salary forecasting model. On this basis, we predict the salary of job seekers based on the work experience, education, and work city provided by the job seeker. By simulation experiments, we show that the effectiveness of our method on the job analysis and recommendation.


## 2. Data Acquisition and Analysis

### 2.1. Data Sources

In this paper, we use the data sets of a job seeker that comes from several major recruitment websites. According to the relevant data, the national online recruitment market reached 10.7 billion yuan in 2019, which has a growth rate of 17.3%. According to the research report on the market development of China’s online recruitment industry in 2020, the current recruitment websites are divided into four categories, namely, comprehensive recruitment model, vertical recruitment model, classified information recruitment model, and emerging recruitment model.The vertical recruitment model is the most efficient recruitment model. It focuses on recruitment services in a certain industry, a specific group of people, or a specific area, which makes it more targeted and professional [13]. Therefore, we select the vertical recruitment model websites as the data sources, which include lagou, liepin, Southern Talent Network, and so on [14]. These data collections include the positions of data analysis, data mining, and software development.


### 2.2. Data Preprocessing

Among the data collected above, there are some unstructured impurity data on work location and salary. In addition, the recruitment data text contains keywords such as job requirements and job benefits [15]. Therefore, we need to preprocess the recruitment data.First, we use the method based on the word graph model to obtain the skill keyword set in the introduction of each recruitment information [16]. Then, we segment the job location in the collected recruitment data. Since most of the data we collect are in different cities, we segment the location by keeping the city information. Third, we unified the data format. We use the upper limit of salary and the lower limit of salary to save separately.


### 2.3. Data Visualization Analysis

In this section, we conduct a detailed visualization analysis of the location of the position, job skill requirements, and the influencing factors of job salary.(1)
We use the preprocessed three types of data to count the work location of each of the recruitment information. In order to improve the display effect, we set the basic unit of the histogram to compress the number of posts.(2)
Based on the preprocessed text keywords, we classify the requirements for talents of the companies into six categories: programming language, major, database, personal ability, office software, and professional skills. We make statistics on the above six types of skills and extract high-frequency skill vocabulary from each category. Then, we will save the keyword frequency of the company’s capability requirements uniformly that we have counted in excel. The saving format is shown in Table1.We consider that there are many types of skills, and there may be some specific associations between the company’s skills requirements for talents in each recruitment data. Therefore, we use a dynamic relationship diagram to display them, where different colors are used to indicate the category of skills. We use 1 to indicate the frequency of skills appearing at the same time in each recruitment data using wires. In order to make the relationship between skills more generalized, we recommend setting thresholds to control the connections between skills, which is shown in equation (1), a and b represent two skills required for a position in recruitment data.(1)Jab=pa∩bpa+pa∩bpb=1,Jab>0.50,Jab≤0.5.(3)
We intercept three types of labels from the data which include education, experience, and salary. First, we process the data hierarchically and store the processed data in excel. The data storage format is shown in Table2. Second, we use a dynamic mulberry chart to show the relationship between these three labels. The extended branch width in the mulberry chart corresponds to the amount of data flow, which is more suitable for visual analysis of data such as flow. Third, we use each endpoint to segment education, experience, and salary can effectively show the impact of different education and work experience on salary.Table 1
Enterprise capability requirements.
LanguageID345201354033156864614211941143162443235584Python1111001Java0110000VBA0000000C++0001100C1000101Shell0000010Scala0000000Go0000010MATLAB0100000Javascript0110101Hive1001000Hbase1000000PS0000001Business0011000Logic1101000Math1101000Computer1100100Financial0010000Table 2
Information interception of academic experience and salary data.
IDSourceTargetValue1Total numberBachelor7162Total numberMaster4323Total numberPh.D.1284Total numberUnlimited education695Total numberSpecialist766BachelorExperience 1–3 years1827BachelorExperience 3–5 years3898BachelorExperience 5–10 years1169BachelorUnlimited experience2910Experience 1–3 yearsBelow 100000 yuan4111Experience 1–3 years100000 yuan–200000 yuan5212Experience 1–3 years200000 yuan–300000 yuan3813Experience 1–3 years300000 yuan–400000 yuan3014Experience 1–3 years400000 yuan–500000 yuan21


## 2.1. Data Sources

In this paper, we use the data sets of a job seeker that comes from several major recruitment websites. According to the relevant data, the national online recruitment market reached 10.7 billion yuan in 2019, which has a growth rate of 17.3%. According to the research report on the market development of China’s online recruitment industry in 2020, the current recruitment websites are divided into four categories, namely, comprehensive recruitment model, vertical recruitment model, classified information recruitment model, and emerging recruitment model.The vertical recruitment model is the most efficient recruitment model. It focuses on recruitment services in a certain industry, a specific group of people, or a specific area, which makes it more targeted and professional [13]. Therefore, we select the vertical recruitment model websites as the data sources, which include lagou, liepin, Southern Talent Network, and so on [14]. These data collections include the positions of data analysis, data mining, and software development.


## 2.2. Data Preprocessing

Among the data collected above, there are some unstructured impurity data on work location and salary. In addition, the recruitment data text contains keywords such as job requirements and job benefits [15]. Therefore, we need to preprocess the recruitment data.First, we use the method based on the word graph model to obtain the skill keyword set in the introduction of each recruitment information [16]. Then, we segment the job location in the collected recruitment data. Since most of the data we collect are in different cities, we segment the location by keeping the city information. Third, we unified the data format. We use the upper limit of salary and the lower limit of salary to save separately.


## 2.3. Data Visualization Analysis

In this section, we conduct a detailed visualization analysis of the location of the position, job skill requirements, and the influencing factors of job salary.(1)
We use the preprocessed three types of data to count the work location of each of the recruitment information. In order to improve the display effect, we set the basic unit of the histogram to compress the number of posts.(2)
Based on the preprocessed text keywords, we classify the requirements for talents of the companies into six categories: programming language, major, database, personal ability, office software, and professional skills. We make statistics on the above six types of skills and extract high-frequency skill vocabulary from each category. Then, we will save the keyword frequency of the company’s capability requirements uniformly that we have counted in excel. The saving format is shown in Table1.We consider that there are many types of skills, and there may be some specific associations between the company’s skills requirements for talents in each recruitment data. Therefore, we use a dynamic relationship diagram to display them, where different colors are used to indicate the category of skills. We use 1 to indicate the frequency of skills appearing at the same time in each recruitment data using wires. In order to make the relationship between skills more generalized, we recommend setting thresholds to control the connections between skills, which is shown in equation (1), a and b represent two skills required for a position in recruitment data.(1)Jab=pa∩bpa+pa∩bpb=1,Jab>0.50,Jab≤0.5.(3)
We intercept three types of labels from the data which include education, experience, and salary. First, we process the data hierarchically and store the processed data in excel. The data storage format is shown in Table2. Second, we use a dynamic mulberry chart to show the relationship between these three labels. The extended branch width in the mulberry chart corresponds to the amount of data flow, which is more suitable for visual analysis of data such as flow. Third, we use each endpoint to segment education, experience, and salary can effectively show the impact of different education and work experience on salary.Table 1
Enterprise capability requirements.
LanguageID345201354033156864614211941143162443235584Python1111001Java0110000VBA0000000C++0001100C1000101Shell0000010Scala0000000Go0000010MATLAB0100000Javascript0110101Hive1001000Hbase1000000PS0000001Business0011000Logic1101000Math1101000Computer1100100Financial0010000Table 2
Information interception of academic experience and salary data.
IDSourceTargetValue1Total numberBachelor7162Total numberMaster4323Total numberPh.D.1284Total numberUnlimited education695Total numberSpecialist766BachelorExperience 1–3 years1827BachelorExperience 3–5 years3898BachelorExperience 5–10 years1169BachelorUnlimited experience2910Experience 1–3 yearsBelow 100000 yuan4111Experience 1–3 years100000 yuan–200000 yuan5212Experience 1–3 years200000 yuan–300000 yuan3813Experience 1–3 years300000 yuan–400000 yuan3014Experience 1–3 years400000 yuan–500000 yuan21


## 3. An Efficient Interpretable Visualization Method of Multidimensional Structural Data Matching

In this section, we propose an efficient Interpretable Visualization method of Multidimensional Structural Data Matching (IVMS-DM), and the specific implementation process shows in Figure1. We first divide the job seekers according to their abilities, and we judge their work directions using the discrimination model. Then, we introduce a matching algorithm based on the job applicant ability table. Finally, we get the visualized graphs on job distribution and skills relation.Figure 1
The specific implementation flow of IVMS-DM.


### 3.1. Discrimination Model

In this subsection, we introduce a discriminant model that ensures the job seeker’s required position highly matches the employees required for the recruitment position. We first divide the personal information provided by the job seekers according to the job seeker’s ability keywords and welfare position keywords are obtained. Then, we generate the job seeker ability table and the job seeker demand table at the same time. We find all frequent itemsets in the database that meet the minimum support threshold based on the recruitment data and generate the association rules. We calculate the support, confidence, and lift of each rule and generate the association rule table, and we further explore the relationship between the skills required for the three types of positions. We filter the actual situation by setting the lower limit of lift and support. Comparing the job seeker’s ability table with the skill association rule table, the result of judging the job direction of the job seeker is finally obtained.


### 3.2. Abilities Extraction and Analysis Based on Job Applicants

In this subsection, we extract and analyze the demand positions based on the abilities of the job applicants. We useUi to represent the ith job seeker. For each job seeker Ui, we use Si to represent the set that the skills mastered by Ui, where Si=Si1,Si2,…,Sij,…,Sin. We use Sij to represent the jth skill of job seeker Ui. In the actual recruitment process of the company, different skills of a job seeker have different effects on whether the job seeker can pass smoothly. For example, data analysis companies want job seekers to master python and hive, and data mining companies want job seekers to master Hadoop and spark. Therefore, in order to describe the role of job seekers’ various skills in job searching accurately, we introduce the weight coefficient Wi, where Wi=Wi1,Wi2,…,Wij,…,Win. As shown in equation (2), aij and dij are the applicant information of Ui in the job seeker ability table A and the job seeker demand table D.(2)Wij=aijPaij∩dijS·Paij·Pdij.According to the value of the weight coefficient, we calculate the similarity of the recruitment positions based on the job skill requirements and the skills of the job seeker, and we add the weight of each skill to the matching results. We sort the final results according to the matching degree by descending.


### 3.3. Demands Extraction and Analysis Based on Job Applicants

In this subsection, we propose a salary prediction model based on the regression method. We forecast the salary of job seekers according to the work experience, education background, and work city provided by the job seekers. We extract the welfare-related feature values ​​in the recruitment information text, and we convert the company welfare-related words into a word frequency matrix. Considering that some words appear frequently in the text but are not important, that is, words with a small amount of information need to reduce their weight. Therefore, we use the term frequency-inverse document frequency (TF-IDF) algorithm to convert words related to corporate welfare into vectors that can be processed, and we use IDF values to grasp the importance of words. For a certain keywordt, its TF value calculation formula is shown in (3), where mij is the frequency of the keyword appearing in the text set. The denominator is the sum of the appearance times of all words in the text set after the word segmentation. The IDF value of the antidocument frequency is shown in (4), where i:tj∈hj represents the number of texts containing the keyword. We finally calculate the TF-IDF value of the keyword and generate a graph of corporate welfare benefits.(3)TFi,j=mij∑j=1kmij,(4)IDFj=logHi:tj∈hj.Furthermore, we calculate the similarity using the job seeker demand table and the relevant feature values of corporate welfare in the recruitment data. Then, the results are sorted according to the similarity of the demands of job seekers’ welfare. We filter out the positions whose salary is greater than the salary predicted by the job seeker, which is the final matching result.


### 3.4. Display Job Matching Analysis Report

According to the job ID matched by the job seeker’s demands, the skills needed for the jobs are obtained from the database. Then we calculate the proportion of each skill in each city. We use a radar chart to display the required skills for job seekers. According to the ideal city of the job seeker, we divide the radar chart and use equation (5) to calculate the probability of finding a job that meets the seeker’s demands in each ideal city. The suggestions based on the above information are given to job seekers. SUM is the total number of positions, and is shown in equation (6). Pos and Neg represent two matching results: Pos is the position based on the needs of job seekers according to the intention of job seekers, and Neg is the position based on the abilities of job seekers according to the needs of enterprises.(5)probability=∑i=0nPi/Pos∗Wii+∑j=0nPj/Neg∗Wjj,(6)SUM=Pos+Neg.


## 3.1. Discrimination Model

In this subsection, we introduce a discriminant model that ensures the job seeker’s required position highly matches the employees required for the recruitment position. We first divide the personal information provided by the job seekers according to the job seeker’s ability keywords and welfare position keywords are obtained. Then, we generate the job seeker ability table and the job seeker demand table at the same time. We find all frequent itemsets in the database that meet the minimum support threshold based on the recruitment data and generate the association rules. We calculate the support, confidence, and lift of each rule and generate the association rule table, and we further explore the relationship between the skills required for the three types of positions. We filter the actual situation by setting the lower limit of lift and support. Comparing the job seeker’s ability table with the skill association rule table, the result of judging the job direction of the job seeker is finally obtained.


## 3.2. Abilities Extraction and Analysis Based on Job Applicants

In this subsection, we extract and analyze the demand positions based on the abilities of the job applicants. We useUi to represent the ith job seeker. For each job seeker Ui, we use Si to represent the set that the skills mastered by Ui, where Si=Si1,Si2,…,Sij,…,Sin. We use Sij to represent the jth skill of job seeker Ui. In the actual recruitment process of the company, different skills of a job seeker have different effects on whether the job seeker can pass smoothly. For example, data analysis companies want job seekers to master python and hive, and data mining companies want job seekers to master Hadoop and spark. Therefore, in order to describe the role of job seekers’ various skills in job searching accurately, we introduce the weight coefficient Wi, where Wi=Wi1,Wi2,…,Wij,…,Win. As shown in equation (2), aij and dij are the applicant information of Ui in the job seeker ability table A and the job seeker demand table D.(2)Wij=aijPaij∩dijS·Paij·Pdij.According to the value of the weight coefficient, we calculate the similarity of the recruitment positions based on the job skill requirements and the skills of the job seeker, and we add the weight of each skill to the matching results. We sort the final results according to the matching degree by descending.


## 3.3. Demands Extraction and Analysis Based on Job Applicants

In this subsection, we propose a salary prediction model based on the regression method. We forecast the salary of job seekers according to the work experience, education background, and work city provided by the job seekers. We extract the welfare-related feature values ​​in the recruitment information text, and we convert the company welfare-related words into a word frequency matrix. Considering that some words appear frequently in the text but are not important, that is, words with a small amount of information need to reduce their weight. Therefore, we use the term frequency-inverse document frequency (TF-IDF) algorithm to convert words related to corporate welfare into vectors that can be processed, and we use IDF values to grasp the importance of words. For a certain keywordt, its TF value calculation formula is shown in (3), where mij is the frequency of the keyword appearing in the text set. The denominator is the sum of the appearance times of all words in the text set after the word segmentation. The IDF value of the antidocument frequency is shown in (4), where i:tj∈hj represents the number of texts containing the keyword. We finally calculate the TF-IDF value of the keyword and generate a graph of corporate welfare benefits.(3)TFi,j=mij∑j=1kmij,(4)IDFj=logHi:tj∈hj.Furthermore, we calculate the similarity using the job seeker demand table and the relevant feature values of corporate welfare in the recruitment data. Then, the results are sorted according to the similarity of the demands of job seekers’ welfare. We filter out the positions whose salary is greater than the salary predicted by the job seeker, which is the final matching result.


## 3.4. Display Job Matching Analysis Report

According to the job ID matched by the job seeker’s demands, the skills needed for the jobs are obtained from the database. Then we calculate the proportion of each skill in each city. We use a radar chart to display the required skills for job seekers. According to the ideal city of the job seeker, we divide the radar chart and use equation (5) to calculate the probability of finding a job that meets the seeker’s demands in each ideal city. The suggestions based on the above information are given to job seekers. SUM is the total number of positions, and is shown in equation (6). Pos and Neg represent two matching results: Pos is the position based on the needs of job seekers according to the intention of job seekers, and Neg is the position based on the abilities of job seekers according to the needs of enterprises.(5)probability=∑i=0nPi/Pos∗Wii+∑j=0nPj/Neg∗Wjj,(6)SUM=Pos+Neg.


## 4. Simulation and Result

### 4.1. Simulation Setting

We download the dataset from a recruitment website [17]. This dataset contains 14936 recruitment data in 20 days that mainly including data analysis, data mining and software development. We select 13273 valid data by preprocessing, and the experimental part uses Python and related libraries.


### 4.2. Recruitment Data Visualization

#### 4.2.1. Analysis of Position Location

We analyze the distribution of enterprises across the country, and we set the basic unit as 50. The national distribution map of enterprises is obtained in Figure2. From the perspective of cities where enterprises are located, Beijing, Shenzhen, and Shanghai are the top three cities in the 660 cities in China which are accounting for 34%, 26%, and 18% respectively. From the perspective of regional distribution of enterprises, enterprises in the Yangtze River Delta and Guangdong Province are more widely distributed which are accounting for 36% and 28% respectively. From the perspective of enterprise-scale, listed companies with more than 2000 people accounted for the largest proportion. These companies are mainly distributed in Beijing and Shanghai, which are accounting for 17% and 15% respectively. From the perspective of the enterprises’ industry, data services and mobile Internet accounted for the largest proportion, which are accounting for 38% and 32%, respectively.Figure 2
National distribution map of enterprises.


#### 4.2.2. Analysis of Post Skill Correlation

The association of the post skills is shown in Figure3. According to the results, we have the following observations: (i) The top three discipline requirements are computer, mathematics, and statistics, and these three positions have similar discipline requirements for employees. In terms of the requirements of the abilities, the three positions also value personal learning ability and communication ability. (ii) There are differences in the importance of different skills for different positions. We can see that the direction of computer science needs to have a solid mathematical foundation, and it also needs to have a strong ability to receive new things. Most enterprises in the data analysis direction need to master python, hive, and SQL at the same time, while enterprises in the data mining direction need to master Python, Hadoop, and spark at the same time. In terms of the programming language of the three positions, Java ranks in the top three. However, MySQL accounts for a large proportion in terms of database.Figure 3
The association of the post skills.


#### 4.2.3. Analysis of the Multidimensional Influencing Factors of Salary

In this subsection, we analyze the multi-dimensional influencing factors of salary of three types of positions which are shown in Figures4–6. From the perspective of educational requirements of enterprises, the proportion of undergraduates in the three types of positions is the largest that accounting for 58%, 43%, and 72%, respectively. Among them, the number of enterprises requiring a master’s degree in data mining is the largest. What’s more, the software development direction requires the number of junior college students to rank second. From the above data, we can see that the software development direction has the lowest education requirements, while the data mining direction has the highest education requirements; From the perspective of experience requirements of enterprises, these three types of positions are generally concentrated in 3–5 years, and the undergraduate education accounts for the largest proportion. Most enterprises generally require 1–3 years of personnel experience for a master degree, but there is generally no experience limitation. From the perspective of salary given by enterprises, the average salary of data mining is the highest, with an annual salary of 0.3-0.4 million, followed by that of data analysis of 0.2-0.3 million, and that of software development of 0.1-0.2 million. From the relationship among education background, experience, and salary, the proportion of undergraduates working in data analysis with an annual salary of 0.2-0.3 million for 3–5 years is the highest. The number of software development graduates with 1–3 years’ salary of 0.1-0.2 million is the largest, and the number of data mining graduates with 1–3 years’ salary of 0.4-0.5 million is the largest. From the perspective of educational requirements of enterprises, the proportion of undergraduates in the three types of positions is the largest that accounting for 58%, 43%, and 72%, respectively. Among them, the number of enterprises requiring a master degree in data mining is the largest. What’s more, the software development direction requires the number of junior college students to rank second. From the above data, we can see that the software development direction has the lowest education requirements, while the data mining direction has the highest education requirements; From the perspective of experience requirements of enterprises, these three types of positions are generally concentrated in 3–5 years, and the undergraduate education accounts for the largest proportion. Most enterprises generally require 1–3 years of personnel experience for a master degree, but there is generally no experience limitation. From the perspective of salary given by enterprises, the average salary of data mining is the highest, with an annual salary of 0.3-0.4 million, followed by that of data analysis of 0.2-0.3 million, and that of software development of 0.1-0.2 million. From the relationship among education background, experience, and salary, the proportion of undergraduates working in data analysis with an annual salary of 0.2-0.3 million for 3–5 years is the highest. The number of software development graduates with 1–3 years’ salary of 0.1-0.2 million is the largest, and the number of data mining graduates with 1–3 years’ salary of 0.4-0.5 million is the largest.Figure 4
Multidimensional influencing factors of data mining salary.Figure 5
Analysis chart of multidimensional influencing factors of software development salary.Figure 6
Data analysis multidimensional influencing factor analysis chart of salary.


### 4.3. Post Matching and Visualization

#### 4.3.1. Division of the Job Seekers’ Information

The job seeker’s personal information is divided into the job seeker’s ability table and the job seeker’s demand table, and the split result is shown in Tables3 and 4.Table 3
Job seeker’s ability table.
Programing languagePython, JavaDatabaseMySQLOffice skillsExcelProfessional skillsSpark, html, CSSPersonal abilityLearning ability, logical ability, and professional abilityLearning directionComputerEducationUndergraduateWorking experience3 yearsTable 4
Job seeker’s demand table.
Ideal cityBeijing, Shanghai, HangzhouWelfareInsurances, meals freeScaleLargeCategoryInternet, e-commerce, entertainment


#### 4.3.2. Discrimination of Job Seeker’s Work Direction

We set the lower limit of life as 1, and the lower limit of support as 0.2. We use the processed data to make the skill and position association rules table and calculate the similarity between the candidate’s ability table and the position skill association rules table. The result shows that the candidate’s direction is data analysis. The table of skill post association rules is shown in Table5.Table 5
Skill and position category association rules table.
AntecedentsConsequentsAntecedent supportConsequent support38MathematicsStatistics0.4081340.1292352HadoopHive0.2196170.14114854SparkHive0.2555020.14114856HadoopSpark0.2196170.25550284Spark, PythonJava0.1923440.263158143Hadoop, PythonSpark0.1669860.255502218Computer, HadoopSpark0.1421050.255502240Hadoop, sparkHive0.1976080.141148246Machine learning, HadoopSpark0.1698560.255502365Machine learning, Hadoop, PythonSpark0.134450.255502404HadoopMachine learning, computer, spark0.2196170.141627405SparkMachine learning, computer, Hadoop0.2555020.11578940SparkHadoop0.2827730.26806743HTMLCSS0.126050.13865580Spark, PythonHadoop0.1571430.26806782Hadoop, PythonSpark0.1432770.28277388Hive, JavaSpark0.1142860.28277398CSS, JavaHTML0.1201680.12605122Spark, computerHadoop0.1655460.268067128Spark, HiveHadoop0.1957980.26806742C++, computerHTML0.2196170.14162748Computer, AndroidJava0.2555020.115789SupportConfidenceLiftConvictionFamily380.1071770.2626032.1190711.188066Data mining520.1181820.5381263.8124881.859495Data mining540.1244020.4868913.4495021.67382Data mining560.1976080.8997823.5216197.428791Data mining840.1033490.5373132.0417911.59253Data mining1430.1478470.8853873.4652786.495742Data mining2180.1291870.9090913.5580528.189474Data mining2400.1090910.5520583.9111911.917328Data mining2460.1569380.9239443.6161849.788765Data mining3650.1229670.9145913.5795788.716826Data mining4040.1066990.4858393.4304161.669463Data mining4050.1066990.4176033.6065711.518226Data mining400.2189080.7741462.8878791.67382Data analysis430.1218490.9666676.9717175.840336Data analysis800.1189080.7566842.8227423.008163Data analysis820.1189080.8299122.9349044.216799Data analysis880.10.8753.0943545.737815Data analysis980.1058820.8811196.990217.351458Data analysis1220.1294120.7817262.9161563.353273Data analysis1280.1537820.7854082.9298913.410807Data analysis420.1066990.4858393.4304161.669463Software development480.1066990.4176033.6065711.518226Software development


#### 4.3.3. Job Matching Based on Job Seeker Competency Table

We calculate the weight of each skill and construct the coefficient table, which is shown in Table6. Then, we calculate similarities of the skills and divide the recruitment positions according to the required skills. Then, the ability table of job seekers is used to match the divided positions. In the process of matching, the coefficient is added to the algorithm, and finally the most suitable position set is generated according to the descending order of the matching degree. The result contains 258 pieces of recruitment information, and the most suitable position set is shown in Table 7.Table 6
skill and construct the coefficient table.
SkillClassificationWeightHadoopData mining1.252852PythonData analysis1.837236PythonData mining1.300227SparkData mining1.6473HadoopData analysis1.319256JavaSoftware development2.23734HiveData analysis2.103453C++Software development1.84724MySQLSoftware development2.4235ExcelData analysis1.36742SQLSoftware development1.2253SparkData analysis1.86534Skill2Classification3Weight4Hadoop, pythonData mining2.423652HTML, MySQLSoftware development1.93652Java, C++Software development1.87234HTML, CSSSoftware development1.34634Oracle, C++Software development1.765345Python, SparkData mining1.628372Linux, CSoftware development1.837236Python, HiveData analysis1.963872MySQL, pythonData analysis1.13267Python, ExcelData mining2.037423BI, HiveData analysis1.46234Hadoop, SparkData mining1.432423Table 7
The most suitable position set.
position_idTitlecompany_URLSkill192375Web front endhttp://www.lagou.com/gongsi/30008.htmlCSS, Mobile terminal, Front end development, HTML51675817Senior Java R&D Engineerhttp://www.lagou.com/gongsi/1686.htmlCSS, Spark, HTML, Java1763729Big data development engineerhttp://www.lagou.com/gongsi/12597.htmlHadoop, Spark, Hive, Java2873774Front end engineer (Beijing/Wuhan)http://www.lagou.com/gongsi/31164.htmlLearning ability, Computer direction, HTML, Javascript, Web front end2976934Big data development engineerhttp://www.lagou.com/gongsi/189298.htmlExcel, Spark, MySQL, Java, Data analysis…………7459738Big data development engineerhttp://www.lagou.com/gongsi/120642419.htmlSpark, python, Algorithm Engineer7459835Front end experthttp://www.lagou.com/gongsi/153849.htmlJava, Logical ability, Computer direction, Excel, MySQL, Javascript7460129Senior data analysthttp://www.lagou.com/gongsi/211706.htmlSpark, python7460663Big data development engineerhttp://www.lagou.com/gongsi/577862.htmlHadoop, hbase, Spark, python7460888Front end senior development engineerhttp://www.lagou.com/gongsi/120644511.htmlCSS, HTML5, CSS3, jQuery, Javascript


#### 4.3.4. Job Matching Based on Job Seeker Demand Table

According to the regression model, the annual salary of job seekers is 213750 yuan.After calculation, the relationship diagram of enterprise welfare is shown in Figure7. The similarity between the job seeker’s demand table and the welfare benefits extracted from the recruitment information is calculated, and the predicted salary of the job seeker is filtered. Finally, 63 positions with the most suitable demand are obtained, and the set of positions with the most suitable demand is shown in Table 8.Figure 7
The relationship diagram of enterprise welfare.Table 8
The set of positions with the most suitable demand.
position_idTitleenterprice_localeenterprice_category4553174Commercial data analystBeijingEntertainment5119472Data visualization R&D Director - platform directionShanghaiEntertainment5120075Data AnalystBeijingEntertainment5120103Data AnalystShanghaiElectronic commerce5120207Senior Data AnalystBeijingEntertainment…………7377471Senior Data AnalystShanghaiEntertainment7388404Big data Development Engineer - live broadcastBeijingMobile Internet7419188Big data Development Engineer - Zhu XiaobangBeijingEntertainment7447755Senior Data AnalystHangzhouMobile Internet7451860Short video data operation managerBeijingEntertainmentposition_identerprice_stageenterprice_peopleMoneyText4553174C2000240000480000Six insurances and one fund, flexible work, free three meals, rental subsidy, flat management, professional Daniel, promotion opportunity, good team atmosphere5119472C2000480000960000Six insurances and one fund, free three meals, rental subsidy, professional Daniel5120075C2000180000360000Six insurances and one fund, flexible work, free three meals, good team atmosphere5120103C1000180000360000Six insurances and one fund, flexible work, free three meals, good team atmosphere5120207C2000180000360000Six insurances and one fund, flexible work, free three meals, good team atmosphere……………7377471C2000180000360000Six insurances and one fund, stock infringement, flexible work, free three meals7388404C2000300000600000Six insurances and one fund, stock infringement, flexible work, free three meals7419188C2000240000480000Six insurances and one fund, flexible work, free three meals, rental subsidy7447755C1000240000360000Six insurances and one fund, flexible work, free three meals, rental subsidy7451860C2000180000360000Six insurances and one fund, flexible work, free three meals, rental subsidy


## 4.1. Simulation Setting

We download the dataset from a recruitment website [17]. This dataset contains 14936 recruitment data in 20 days that mainly including data analysis, data mining and software development. We select 13273 valid data by preprocessing, and the experimental part uses Python and related libraries.


## 4.2. Recruitment Data Visualization

### 4.2.1. Analysis of Position Location

We analyze the distribution of enterprises across the country, and we set the basic unit as 50. The national distribution map of enterprises is obtained in Figure2. From the perspective of cities where enterprises are located, Beijing, Shenzhen, and Shanghai are the top three cities in the 660 cities in China which are accounting for 34%, 26%, and 18% respectively. From the perspective of regional distribution of enterprises, enterprises in the Yangtze River Delta and Guangdong Province are more widely distributed which are accounting for 36% and 28% respectively. From the perspective of enterprise-scale, listed companies with more than 2000 people accounted for the largest proportion. These companies are mainly distributed in Beijing and Shanghai, which are accounting for 17% and 15% respectively. From the perspective of the enterprises’ industry, data services and mobile Internet accounted for the largest proportion, which are accounting for 38% and 32%, respectively.Figure 2
National distribution map of enterprises.


### 4.2.2. Analysis of Post Skill Correlation

The association of the post skills is shown in Figure3. According to the results, we have the following observations: (i) The top three discipline requirements are computer, mathematics, and statistics, and these three positions have similar discipline requirements for employees. In terms of the requirements of the abilities, the three positions also value personal learning ability and communication ability. (ii) There are differences in the importance of different skills for different positions. We can see that the direction of computer science needs to have a solid mathematical foundation, and it also needs to have a strong ability to receive new things. Most enterprises in the data analysis direction need to master python, hive, and SQL at the same time, while enterprises in the data mining direction need to master Python, Hadoop, and spark at the same time. In terms of the programming language of the three positions, Java ranks in the top three. However, MySQL accounts for a large proportion in terms of database.Figure 3
The association of the post skills.


### 4.2.3. Analysis of the Multidimensional Influencing Factors of Salary

In this subsection, we analyze the multi-dimensional influencing factors of salary of three types of positions which are shown in Figures4–6. From the perspective of educational requirements of enterprises, the proportion of undergraduates in the three types of positions is the largest that accounting for 58%, 43%, and 72%, respectively. Among them, the number of enterprises requiring a master’s degree in data mining is the largest. What’s more, the software development direction requires the number of junior college students to rank second. From the above data, we can see that the software development direction has the lowest education requirements, while the data mining direction has the highest education requirements; From the perspective of experience requirements of enterprises, these three types of positions are generally concentrated in 3–5 years, and the undergraduate education accounts for the largest proportion. Most enterprises generally require 1–3 years of personnel experience for a master degree, but there is generally no experience limitation. From the perspective of salary given by enterprises, the average salary of data mining is the highest, with an annual salary of 0.3-0.4 million, followed by that of data analysis of 0.2-0.3 million, and that of software development of 0.1-0.2 million. From the relationship among education background, experience, and salary, the proportion of undergraduates working in data analysis with an annual salary of 0.2-0.3 million for 3–5 years is the highest. The number of software development graduates with 1–3 years’ salary of 0.1-0.2 million is the largest, and the number of data mining graduates with 1–3 years’ salary of 0.4-0.5 million is the largest. From the perspective of educational requirements of enterprises, the proportion of undergraduates in the three types of positions is the largest that accounting for 58%, 43%, and 72%, respectively. Among them, the number of enterprises requiring a master degree in data mining is the largest. What’s more, the software development direction requires the number of junior college students to rank second. From the above data, we can see that the software development direction has the lowest education requirements, while the data mining direction has the highest education requirements; From the perspective of experience requirements of enterprises, these three types of positions are generally concentrated in 3–5 years, and the undergraduate education accounts for the largest proportion. Most enterprises generally require 1–3 years of personnel experience for a master degree, but there is generally no experience limitation. From the perspective of salary given by enterprises, the average salary of data mining is the highest, with an annual salary of 0.3-0.4 million, followed by that of data analysis of 0.2-0.3 million, and that of software development of 0.1-0.2 million. From the relationship among education background, experience, and salary, the proportion of undergraduates working in data analysis with an annual salary of 0.2-0.3 million for 3–5 years is the highest. The number of software development graduates with 1–3 years’ salary of 0.1-0.2 million is the largest, and the number of data mining graduates with 1–3 years’ salary of 0.4-0.5 million is the largest.Figure 4
Multidimensional influencing factors of data mining salary.Figure 5
Analysis chart of multidimensional influencing factors of software development salary.Figure 6
Data analysis multidimensional influencing factor analysis chart of salary.


## 4.2.1. Analysis of Position Location

We analyze the distribution of enterprises across the country, and we set the basic unit as 50. The national distribution map of enterprises is obtained in Figure2. From the perspective of cities where enterprises are located, Beijing, Shenzhen, and Shanghai are the top three cities in the 660 cities in China which are accounting for 34%, 26%, and 18% respectively. From the perspective of regional distribution of enterprises, enterprises in the Yangtze River Delta and Guangdong Province are more widely distributed which are accounting for 36% and 28% respectively. From the perspective of enterprise-scale, listed companies with more than 2000 people accounted for the largest proportion. These companies are mainly distributed in Beijing and Shanghai, which are accounting for 17% and 15% respectively. From the perspective of the enterprises’ industry, data services and mobile Internet accounted for the largest proportion, which are accounting for 38% and 32%, respectively.Figure 2
National distribution map of enterprises.


## 4.2.2. Analysis of Post Skill Correlation

The association of the post skills is shown in Figure3. According to the results, we have the following observations: (i) The top three discipline requirements are computer, mathematics, and statistics, and these three positions have similar discipline requirements for employees. In terms of the requirements of the abilities, the three positions also value personal learning ability and communication ability. (ii) There are differences in the importance of different skills for different positions. We can see that the direction of computer science needs to have a solid mathematical foundation, and it also needs to have a strong ability to receive new things. Most enterprises in the data analysis direction need to master python, hive, and SQL at the same time, while enterprises in the data mining direction need to master Python, Hadoop, and spark at the same time. In terms of the programming language of the three positions, Java ranks in the top three. However, MySQL accounts for a large proportion in terms of database.Figure 3
The association of the post skills.


## 4.2.3. Analysis of the Multidimensional Influencing Factors of Salary

In this subsection, we analyze the multi-dimensional influencing factors of salary of three types of positions which are shown in Figures4–6. From the perspective of educational requirements of enterprises, the proportion of undergraduates in the three types of positions is the largest that accounting for 58%, 43%, and 72%, respectively. Among them, the number of enterprises requiring a master’s degree in data mining is the largest. What’s more, the software development direction requires the number of junior college students to rank second. From the above data, we can see that the software development direction has the lowest education requirements, while the data mining direction has the highest education requirements; From the perspective of experience requirements of enterprises, these three types of positions are generally concentrated in 3–5 years, and the undergraduate education accounts for the largest proportion. Most enterprises generally require 1–3 years of personnel experience for a master degree, but there is generally no experience limitation. From the perspective of salary given by enterprises, the average salary of data mining is the highest, with an annual salary of 0.3-0.4 million, followed by that of data analysis of 0.2-0.3 million, and that of software development of 0.1-0.2 million. From the relationship among education background, experience, and salary, the proportion of undergraduates working in data analysis with an annual salary of 0.2-0.3 million for 3–5 years is the highest. The number of software development graduates with 1–3 years’ salary of 0.1-0.2 million is the largest, and the number of data mining graduates with 1–3 years’ salary of 0.4-0.5 million is the largest. From the perspective of educational requirements of enterprises, the proportion of undergraduates in the three types of positions is the largest that accounting for 58%, 43%, and 72%, respectively. Among them, the number of enterprises requiring a master degree in data mining is the largest. What’s more, the software development direction requires the number of junior college students to rank second. From the above data, we can see that the software development direction has the lowest education requirements, while the data mining direction has the highest education requirements; From the perspective of experience requirements of enterprises, these three types of positions are generally concentrated in 3–5 years, and the undergraduate education accounts for the largest proportion. Most enterprises generally require 1–3 years of personnel experience for a master degree, but there is generally no experience limitation. From the perspective of salary given by enterprises, the average salary of data mining is the highest, with an annual salary of 0.3-0.4 million, followed by that of data analysis of 0.2-0.3 million, and that of software development of 0.1-0.2 million. From the relationship among education background, experience, and salary, the proportion of undergraduates working in data analysis with an annual salary of 0.2-0.3 million for 3–5 years is the highest. The number of software development graduates with 1–3 years’ salary of 0.1-0.2 million is the largest, and the number of data mining graduates with 1–3 years’ salary of 0.4-0.5 million is the largest.Figure 4
Multidimensional influencing factors of data mining salary.Figure 5
Analysis chart of multidimensional influencing factors of software development salary.Figure 6
Data analysis multidimensional influencing factor analysis chart of salary.


## 4.3. Post Matching and Visualization

### 4.3.1. Division of the Job Seekers’ Information

The job seeker’s personal information is divided into the job seeker’s ability table and the job seeker’s demand table, and the split result is shown in Tables3 and 4.Table 3
Job seeker’s ability table.
Programing languagePython, JavaDatabaseMySQLOffice skillsExcelProfessional skillsSpark, html, CSSPersonal abilityLearning ability, logical ability, and professional abilityLearning directionComputerEducationUndergraduateWorking experience3 yearsTable 4
Job seeker’s demand table.
Ideal cityBeijing, Shanghai, HangzhouWelfareInsurances, meals freeScaleLargeCategoryInternet, e-commerce, entertainment


### 4.3.2. Discrimination of Job Seeker’s Work Direction

We set the lower limit of life as 1, and the lower limit of support as 0.2. We use the processed data to make the skill and position association rules table and calculate the similarity between the candidate’s ability table and the position skill association rules table. The result shows that the candidate’s direction is data analysis. The table of skill post association rules is shown in Table5.Table 5
Skill and position category association rules table.
AntecedentsConsequentsAntecedent supportConsequent support38MathematicsStatistics0.4081340.1292352HadoopHive0.2196170.14114854SparkHive0.2555020.14114856HadoopSpark0.2196170.25550284Spark, PythonJava0.1923440.263158143Hadoop, PythonSpark0.1669860.255502218Computer, HadoopSpark0.1421050.255502240Hadoop, sparkHive0.1976080.141148246Machine learning, HadoopSpark0.1698560.255502365Machine learning, Hadoop, PythonSpark0.134450.255502404HadoopMachine learning, computer, spark0.2196170.141627405SparkMachine learning, computer, Hadoop0.2555020.11578940SparkHadoop0.2827730.26806743HTMLCSS0.126050.13865580Spark, PythonHadoop0.1571430.26806782Hadoop, PythonSpark0.1432770.28277388Hive, JavaSpark0.1142860.28277398CSS, JavaHTML0.1201680.12605122Spark, computerHadoop0.1655460.268067128Spark, HiveHadoop0.1957980.26806742C++, computerHTML0.2196170.14162748Computer, AndroidJava0.2555020.115789SupportConfidenceLiftConvictionFamily380.1071770.2626032.1190711.188066Data mining520.1181820.5381263.8124881.859495Data mining540.1244020.4868913.4495021.67382Data mining560.1976080.8997823.5216197.428791Data mining840.1033490.5373132.0417911.59253Data mining1430.1478470.8853873.4652786.495742Data mining2180.1291870.9090913.5580528.189474Data mining2400.1090910.5520583.9111911.917328Data mining2460.1569380.9239443.6161849.788765Data mining3650.1229670.9145913.5795788.716826Data mining4040.1066990.4858393.4304161.669463Data mining4050.1066990.4176033.6065711.518226Data mining400.2189080.7741462.8878791.67382Data analysis430.1218490.9666676.9717175.840336Data analysis800.1189080.7566842.8227423.008163Data analysis820.1189080.8299122.9349044.216799Data analysis880.10.8753.0943545.737815Data analysis980.1058820.8811196.990217.351458Data analysis1220.1294120.7817262.9161563.353273Data analysis1280.1537820.7854082.9298913.410807Data analysis420.1066990.4858393.4304161.669463Software development480.1066990.4176033.6065711.518226Software development


### 4.3.3. Job Matching Based on Job Seeker Competency Table

We calculate the weight of each skill and construct the coefficient table, which is shown in Table6. Then, we calculate similarities of the skills and divide the recruitment positions according to the required skills. Then, the ability table of job seekers is used to match the divided positions. In the process of matching, the coefficient is added to the algorithm, and finally the most suitable position set is generated according to the descending order of the matching degree. The result contains 258 pieces of recruitment information, and the most suitable position set is shown in Table 7.Table 6
skill and construct the coefficient table.
SkillClassificationWeightHadoopData mining1.252852PythonData analysis1.837236PythonData mining1.300227SparkData mining1.6473HadoopData analysis1.319256JavaSoftware development2.23734HiveData analysis2.103453C++Software development1.84724MySQLSoftware development2.4235ExcelData analysis1.36742SQLSoftware development1.2253SparkData analysis1.86534Skill2Classification3Weight4Hadoop, pythonData mining2.423652HTML, MySQLSoftware development1.93652Java, C++Software development1.87234HTML, CSSSoftware development1.34634Oracle, C++Software development1.765345Python, SparkData mining1.628372Linux, CSoftware development1.837236Python, HiveData analysis1.963872MySQL, pythonData analysis1.13267Python, ExcelData mining2.037423BI, HiveData analysis1.46234Hadoop, SparkData mining1.432423Table 7
The most suitable position set.
position_idTitlecompany_URLSkill192375Web front endhttp://www.lagou.com/gongsi/30008.htmlCSS, Mobile terminal, Front end development, HTML51675817Senior Java R&D Engineerhttp://www.lagou.com/gongsi/1686.htmlCSS, Spark, HTML, Java1763729Big data development engineerhttp://www.lagou.com/gongsi/12597.htmlHadoop, Spark, Hive, Java2873774Front end engineer (Beijing/Wuhan)http://www.lagou.com/gongsi/31164.htmlLearning ability, Computer direction, HTML, Javascript, Web front end2976934Big data development engineerhttp://www.lagou.com/gongsi/189298.htmlExcel, Spark, MySQL, Java, Data analysis…………7459738Big data development engineerhttp://www.lagou.com/gongsi/120642419.htmlSpark, python, Algorithm Engineer7459835Front end experthttp://www.lagou.com/gongsi/153849.htmlJava, Logical ability, Computer direction, Excel, MySQL, Javascript7460129Senior data analysthttp://www.lagou.com/gongsi/211706.htmlSpark, python7460663Big data development engineerhttp://www.lagou.com/gongsi/577862.htmlHadoop, hbase, Spark, python7460888Front end senior development engineerhttp://www.lagou.com/gongsi/120644511.htmlCSS, HTML5, CSS3, jQuery, Javascript


### 4.3.4. Job Matching Based on Job Seeker Demand Table

According to the regression model, the annual salary of job seekers is 213750 yuan.After calculation, the relationship diagram of enterprise welfare is shown in Figure7. The similarity between the job seeker’s demand table and the welfare benefits extracted from the recruitment information is calculated, and the predicted salary of the job seeker is filtered. Finally, 63 positions with the most suitable demand are obtained, and the set of positions with the most suitable demand is shown in Table 8.Figure 7
The relationship diagram of enterprise welfare.Table 8
The set of positions with the most suitable demand.
position_idTitleenterprice_localeenterprice_category4553174Commercial data analystBeijingEntertainment5119472Data visualization R&D Director - platform directionShanghaiEntertainment5120075Data AnalystBeijingEntertainment5120103Data AnalystShanghaiElectronic commerce5120207Senior Data AnalystBeijingEntertainment…………7377471Senior Data AnalystShanghaiEntertainment7388404Big data Development Engineer - live broadcastBeijingMobile Internet7419188Big data Development Engineer - Zhu XiaobangBeijingEntertainment7447755Senior Data AnalystHangzhouMobile Internet7451860Short video data operation managerBeijingEntertainmentposition_identerprice_stageenterprice_peopleMoneyText4553174C2000240000480000Six insurances and one fund, flexible work, free three meals, rental subsidy, flat management, professional Daniel, promotion opportunity, good team atmosphere5119472C2000480000960000Six insurances and one fund, free three meals, rental subsidy, professional Daniel5120075C2000180000360000Six insurances and one fund, flexible work, free three meals, good team atmosphere5120103C1000180000360000Six insurances and one fund, flexible work, free three meals, good team atmosphere5120207C2000180000360000Six insurances and one fund, flexible work, free three meals, good team atmosphere……………7377471C2000180000360000Six insurances and one fund, stock infringement, flexible work, free three meals7388404C2000300000600000Six insurances and one fund, stock infringement, flexible work, free three meals7419188C2000240000480000Six insurances and one fund, flexible work, free three meals, rental subsidy7447755C1000240000360000Six insurances and one fund, flexible work, free three meals, rental subsidy7451860C2000180000360000Six insurances and one fund, flexible work, free three meals, rental subsidy


## 4.3.1. Division of the Job Seekers’ Information

The job seeker’s personal information is divided into the job seeker’s ability table and the job seeker’s demand table, and the split result is shown in Tables3 and 4.Table 3
Job seeker’s ability table.
Programing languagePython, JavaDatabaseMySQLOffice skillsExcelProfessional skillsSpark, html, CSSPersonal abilityLearning ability, logical ability, and professional abilityLearning directionComputerEducationUndergraduateWorking experience3 yearsTable 4
Job seeker’s demand table.
Ideal cityBeijing, Shanghai, HangzhouWelfareInsurances, meals freeScaleLargeCategoryInternet, e-commerce, entertainment


## 4.3.2. Discrimination of Job Seeker’s Work Direction

We set the lower limit of life as 1, and the lower limit of support as 0.2. We use the processed data to make the skill and position association rules table and calculate the similarity between the candidate’s ability table and the position skill association rules table. The result shows that the candidate’s direction is data analysis. The table of skill post association rules is shown in Table5.Table 5
Skill and position category association rules table.
AntecedentsConsequentsAntecedent supportConsequent support38MathematicsStatistics0.4081340.1292352HadoopHive0.2196170.14114854SparkHive0.2555020.14114856HadoopSpark0.2196170.25550284Spark, PythonJava0.1923440.263158143Hadoop, PythonSpark0.1669860.255502218Computer, HadoopSpark0.1421050.255502240Hadoop, sparkHive0.1976080.141148246Machine learning, HadoopSpark0.1698560.255502365Machine learning, Hadoop, PythonSpark0.134450.255502404HadoopMachine learning, computer, spark0.2196170.141627405SparkMachine learning, computer, Hadoop0.2555020.11578940SparkHadoop0.2827730.26806743HTMLCSS0.126050.13865580Spark, PythonHadoop0.1571430.26806782Hadoop, PythonSpark0.1432770.28277388Hive, JavaSpark0.1142860.28277398CSS, JavaHTML0.1201680.12605122Spark, computerHadoop0.1655460.268067128Spark, HiveHadoop0.1957980.26806742C++, computerHTML0.2196170.14162748Computer, AndroidJava0.2555020.115789SupportConfidenceLiftConvictionFamily380.1071770.2626032.1190711.188066Data mining520.1181820.5381263.8124881.859495Data mining540.1244020.4868913.4495021.67382Data mining560.1976080.8997823.5216197.428791Data mining840.1033490.5373132.0417911.59253Data mining1430.1478470.8853873.4652786.495742Data mining2180.1291870.9090913.5580528.189474Data mining2400.1090910.5520583.9111911.917328Data mining2460.1569380.9239443.6161849.788765Data mining3650.1229670.9145913.5795788.716826Data mining4040.1066990.4858393.4304161.669463Data mining4050.1066990.4176033.6065711.518226Data mining400.2189080.7741462.8878791.67382Data analysis430.1218490.9666676.9717175.840336Data analysis800.1189080.7566842.8227423.008163Data analysis820.1189080.8299122.9349044.216799Data analysis880.10.8753.0943545.737815Data analysis980.1058820.8811196.990217.351458Data analysis1220.1294120.7817262.9161563.353273Data analysis1280.1537820.7854082.9298913.410807Data analysis420.1066990.4858393.4304161.669463Software development480.1066990.4176033.6065711.518226Software development


## 4.3.3. Job Matching Based on Job Seeker Competency Table

We calculate the weight of each skill and construct the coefficient table, which is shown in Table6. Then, we calculate similarities of the skills and divide the recruitment positions according to the required skills. Then, the ability table of job seekers is used to match the divided positions. In the process of matching, the coefficient is added to the algorithm, and finally the most suitable position set is generated according to the descending order of the matching degree. The result contains 258 pieces of recruitment information, and the most suitable position set is shown in Table 7.Table 6
skill and construct the coefficient table.
SkillClassificationWeightHadoopData mining1.252852PythonData analysis1.837236PythonData mining1.300227SparkData mining1.6473HadoopData analysis1.319256JavaSoftware development2.23734HiveData analysis2.103453C++Software development1.84724MySQLSoftware development2.4235ExcelData analysis1.36742SQLSoftware development1.2253SparkData analysis1.86534Skill2Classification3Weight4Hadoop, pythonData mining2.423652HTML, MySQLSoftware development1.93652Java, C++Software development1.87234HTML, CSSSoftware development1.34634Oracle, C++Software development1.765345Python, SparkData mining1.628372Linux, CSoftware development1.837236Python, HiveData analysis1.963872MySQL, pythonData analysis1.13267Python, ExcelData mining2.037423BI, HiveData analysis1.46234Hadoop, SparkData mining1.432423Table 7
The most suitable position set.
position_idTitlecompany_URLSkill192375Web front endhttp://www.lagou.com/gongsi/30008.htmlCSS, Mobile terminal, Front end development, HTML51675817Senior Java R&D Engineerhttp://www.lagou.com/gongsi/1686.htmlCSS, Spark, HTML, Java1763729Big data development engineerhttp://www.lagou.com/gongsi/12597.htmlHadoop, Spark, Hive, Java2873774Front end engineer (Beijing/Wuhan)http://www.lagou.com/gongsi/31164.htmlLearning ability, Computer direction, HTML, Javascript, Web front end2976934Big data development engineerhttp://www.lagou.com/gongsi/189298.htmlExcel, Spark, MySQL, Java, Data analysis…………7459738Big data development engineerhttp://www.lagou.com/gongsi/120642419.htmlSpark, python, Algorithm Engineer7459835Front end experthttp://www.lagou.com/gongsi/153849.htmlJava, Logical ability, Computer direction, Excel, MySQL, Javascript7460129Senior data analysthttp://www.lagou.com/gongsi/211706.htmlSpark, python7460663Big data development engineerhttp://www.lagou.com/gongsi/577862.htmlHadoop, hbase, Spark, python7460888Front end senior development engineerhttp://www.lagou.com/gongsi/120644511.htmlCSS, HTML5, CSS3, jQuery, Javascript


## 4.3.4. Job Matching Based on Job Seeker Demand Table

According to the regression model, the annual salary of job seekers is 213750 yuan.After calculation, the relationship diagram of enterprise welfare is shown in Figure7. The similarity between the job seeker’s demand table and the welfare benefits extracted from the recruitment information is calculated, and the predicted salary of the job seeker is filtered. Finally, 63 positions with the most suitable demand are obtained, and the set of positions with the most suitable demand is shown in Table 8.Figure 7
The relationship diagram of enterprise welfare.Table 8
The set of positions with the most suitable demand.
position_idTitleenterprice_localeenterprice_category4553174Commercial data analystBeijingEntertainment5119472Data visualization R&D Director - platform directionShanghaiEntertainment5120075Data AnalystBeijingEntertainment5120103Data AnalystShanghaiElectronic commerce5120207Senior Data AnalystBeijingEntertainment…………7377471Senior Data AnalystShanghaiEntertainment7388404Big data Development Engineer - live broadcastBeijingMobile Internet7419188Big data Development Engineer - Zhu XiaobangBeijingEntertainment7447755Senior Data AnalystHangzhouMobile Internet7451860Short video data operation managerBeijingEntertainmentposition_identerprice_stageenterprice_peopleMoneyText4553174C2000240000480000Six insurances and one fund, flexible work, free three meals, rental subsidy, flat management, professional Daniel, promotion opportunity, good team atmosphere5119472C2000480000960000Six insurances and one fund, free three meals, rental subsidy, professional Daniel5120075C2000180000360000Six insurances and one fund, flexible work, free three meals, good team atmosphere5120103C1000180000360000Six insurances and one fund, flexible work, free three meals, good team atmosphere5120207C2000180000360000Six insurances and one fund, flexible work, free three meals, good team atmosphere……………7377471C2000180000360000Six insurances and one fund, stock infringement, flexible work, free three meals7388404C2000300000600000Six insurances and one fund, stock infringement, flexible work, free three meals7419188C2000240000480000Six insurances and one fund, flexible work, free three meals, rental subsidy7447755C1000240000360000Six insurances and one fund, flexible work, free three meals, rental subsidy7451860C2000180000360000Six insurances and one fund, flexible work, free three meals, rental subsidy


## 5. Conclusions

In this paper, we propose a visualization method of multidimensional matching between job seekers and positions. First, we use visualization to analyze the key information of the position in detail and give the analysis results. Then, we match the recruitment positions from two perspectives: one is based on the ability of the job seeker, and the other is based on the needs of the job seeker. According to the matching results, it provides targeted professional suggestions to job seekers and plays the role of the website as a link between enterprises and job seekers. Finally, we visually analyze the three types of job recruitment data in salary, geographical location, and skills. According to the abilities and demands of job seekers, two comprehensive analysis charts are generated which include post display, multidimensional feature analysis chart and analysis report of job seekers’ abilities. The experiment results show that our method can achieve the expected effect. At the same time, there are also shortcomings. The data source of this article has certain limitations. The selected experimental testers do not cover all types of user groups of recruitment websites, which leads to the limited representative of the testers. The research object selection is limited to a kind of comprehensive recruitment website. However, the development of the online recruitment industry is rapid, and a variety of new online recruitment models have emerged at present. Our future research should cover a variety of domestic and foreign recruitment websites and explore job seekers’ satisfaction with the website and the success rate of job seekers in the recruitment website. Only in this way can we study and analyze job seekers and recruitment information in a wider range and on a deeper level.



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*Source: 2215280-2021-12-24.xml*