CN111062692A - Data mining-based campus recruitment guidance method and module - Google Patents
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Abstract
The invention relates to a data mining-based campus recruitment method and device. The method comprises the steps of collecting personal resume information of a student who applies for, performing word segmentation on the collected personal resume information of the student who applies for, splitting a resume part with time information removed into independent semantic modules, performing knowledge labeling word segmentation and semantic reasoning analysis on the split independent semantic modules, extracting resume data in the personal resume in a smooth aggregation, data generalization and standardization mode, classifying according to the resume data, generating feature labels according to the classification, constructing a quality model of the student according to the feature labels, and calculating the similarity of different students who apply for according to the quality model of the student, so that employment information of similar students is obtained.
Description
Technical Field
The invention belongs to the technical field of campus recruitment information interaction, and particularly relates to a data mining-based campus recruitment awareness method and module.
Background
The recruitment of human resources is carried out on a plurality of related technical markets, matching is mainly carried out according to evaluation results in China, and matching is carried out by companies by adopting resume analysis algorithms, however, the conventional human resource recruitment method has the problems that resumes tend to be homogeneous for students, the algorithm efficiency of the method is poor for student groups, a text mining technology is easy to crack, and data is relatively subjective; the content of learning in the existing human resource recruitment method is a means, can not be used as the standard of talent recommendation, has long period and is disconnected from the reality; in addition, the existing human resource recruitment systems have the defect of long information updating period, and resume updating and learning progress of users are low-frequency operation.
However, evaluation does not truly reflect one's ability to solve a problem by simple question and answer, and evaluation cannot be reused on one person. Traditional resumes provide limited useful information. These problems are particularly acute for university graduates. College students do not have enough past experience data, and resume information rarely has the ability to correlate data in addition to knowledge information.
Patent document CN 106096015A discloses a method for solving the problem of passing through an information wall between people who are subjects and objects of each other in a social network system in a big data platform environment. The personalized recommendation method for the bidirectional information feedback of the host and the object takes the host and the object as evaluation objects, establishes a user evaluation matrix and a project evaluation matrix from two directions, establishes a bidirectional recommendation mode of the host and the object by using a message feedback mechanism, and performs secondary matching fusion on a bidirectional similarity matrix by using a matching technology.
However, in the prior art, in the process of employment, in the face of a large amount of recruitment information of employment websites, graduates cannot accurately locate which units are suitable for themselves, and sometimes hope to locate their job hunting intentions through employment situations of classmates and scholars in the same or similar fields.
Disclosure of Invention
Therefore, aiming at the defects of the prior art, the invention provides a method for establishing the personalized recommendation of the campus recruitment information through data mining.
The purpose of the invention can be realized by the following technical scheme:
a campus recruitment guidance method based on data mining is characterized in that: the method comprises the following steps:
s1, collecting personal resume information of the employing students;
specifically, in the step S1, the personal resume information includes sex, academic history, specialty, school grade, language level, computer level, student cadre history, biographical place, desired work place, practice history and practice history of the recruitment student from the time of entering the society to the time of entering the society.
S2, performing word segmentation on the collected personal resume information of the recruitment student, and splitting the resume part without the time information into independent semantic modules;
s3, carrying out knowledge labeling word segmentation and semantic reasoning analysis on the split independent semantic modules, extracting resume data in the personal resume in a smooth aggregation, data generalization and standardization mode, classifying according to the resume data, and generating a feature label according to the classification;
specifically, in the step S3, the one or more feature labels are assigned.
S4, constructing a quality model of the student;
s5, calculating the similarity of different employing students according to the quality models of the students, and acquiring employment information of the similar students;
specifically, in the steps S4 and S5, a quality model of the employing student is constructed according to resume feature tags of the employing student, wherein feature vectors of the resume feature tags of the employing student are
And acquiring the weight vector of the application students for industry selection
；
And acquiring the similarity of the employing students by calculating the similarity of the applying student quality model, and acquiring the employment information of the similar students according to the similarity of the employing students.
The invention also provides a data mining-based campus recruitment guidance device, which is characterized in that:
the acquisition module is used for acquiring personal resume information of the employing students;
specifically, in the collection module, the personal resume information includes sex, academic history, specialty, school grade, language level, computer level, student cadre experience, biographical place, desired work place, practice experience and practice experience of the applicable student during the period from admission to social entrance.
And the preprocessing module is used for segmenting the personal resume information of the acquired recruitment students and splitting the resume part from which the time information is removed into independent semantic modules.
The classification module is used for carrying out knowledge labeling word segmentation and semantic reasoning analysis on the separated semantic modules, extracting resume data in the personal resume in a smooth aggregation, data generalization and standardization mode, classifying according to the resume data and generating a characteristic label according to the classification;
in particular, in the classification module, the one or more feature labels are assigned values.
The model module is used for establishing a quality model of the student according to the assignment of each feature label in the personal resume information of the student;
and the calculation module is used for calculating the similarity of different employing students according to the quality models of the students so as to acquire employment information of the similar students.
Specifically, in the calculation module, a quality model of the recruitment student is constructed according to resume feature tags of the recruitment student, wherein feature vectors of the resume feature tags of the recruitment student are
；
Acquiring weight vector of application students for industry selection
；
And acquiring the similarity of the employing students by calculating the similarity of the applying student quality model, and acquiring the employment information of the similar students according to the similarity of the employing students.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. In the drawings:
fig. 1 illustrates a flow diagram of a method for data mining-based campus recruitment guidance;
fig. 2 illustrates an apparatus block diagram of a data mining-based campus recruitment guide.
Examples
In the following description, for purposes of explanation and not limitation, examples of method steps and modules of the system are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details.
The purpose of the invention can be realized by the following technical scheme, as shown in figure 1:
a data mining-based campus recruitment guidance method specifically comprises the following steps:
collecting personal resume information of an employing student, wherein the personal resume information comprises sex, a study history, a specialty, school achievements, language level, computer level, student cadre experience, a place of birth, a desired work place, practice experience and practice experience of the employing student in the period from admission to social entrance;
segmenting the personal resume information of the acquired recruitment student, and segmenting the resume part from which the time information is removed into independent semantic modules;
carrying out knowledge labeling word segmentation and semantic reasoning analysis on the semantic modules which are split into independent modules, extracting characteristics of sex, academic calendar, specialty, school score, language level, computer level, student cadre experience, biographical place, expected work place, practice experience and the like in the personal resume in a smooth aggregation, data generalization and standardization mode, classifying according to resume data, and generating a characteristic label according to the classification; and assigning a value to the one or more characteristics.
And constructing a quality model of the student according to each parameter in the personal resume information of the student.
Specifically, in the present example, in the process of assigning the value to the one or more feature tags of the personal resume information of the applicable student, the specific flow is as follows:
(1) and (3) assigning a gender label: according to the actual requirements of recruitment enterprises, different jobs have certain requirements on gender, and specifically, technical work enterprises tend to be more male, so in the embodiment, the gender is also assigned, the male assignment is 1, and the female assignment is 2.
(2) Assigning a calendar label: since the application object belongs to the college graduate, the assignment of the academic calendar is divided into doctor 5, master 3, subject 2 and major 1 according to the importance degree of the academic calendar in the embodiment.
(3) Student achievement label: in the processes of screening resumes and interviewing, the recruitment enterprise requires to check the achievement lists of the students so as to know the subject of the students to be recruited and the achievement of the students to be recruited. In this embodiment, the scores of the students are divided into five standards of fail, pass, medium, good and excellent, and for different standards, specifically, the score is assigned to 1.5 for excellent, 1 for good, 0.75 for medium, 0.5 for pass and 0 for fail.
(4) Assigning a practice label: the practice in different units and even different posts can judge the professional direction and ability of a student, and the recruitment enterprise can take a good look at the practice experience of the student. Therefore, in the embodiment, the practice units of the applicable students are extracted according to the practice experiences filled in the resumes by the applicable students. Specifically, the applicable student practice units are divided into organ and public institution, three-resource units, nationality enterprises, civil enterprises and other enterprises, and are all assigned to 1.
(5) English grade label: recruitment enterprises, particularly foreign enterprises and joint venture enterprises, can also clearly require English certificates in recruitment requirements, and particularly, in the recruitment process of domestic enterprises, the four-six English grades, the four-fourth professional English grade, the eight professional English grade, and the blessing and the Yasi are used as standards, so that the English grades of students to be recruited are assigned as follows, wherein the four-English grades are 1, the six English grades are 2, the four-professional English grades are 2, the eight professional English grades are 3, the blessing is 2, and the Yasi is 2.
(6) Computer-level tags: similar to the English rating labels, the computer rating case emphasizes the student's computer capabilities. Specifically, the first, second, third and fourth computer-level labels are respectively assigned to 1, 1.5, 2 and 3 according to the national computer-level examination.
(7) Winning case label: the prize winning condition is also an important basis for the recruitment enterprise to judge the students. Specifically, the winning categories are classified into a college level, a school level, a provincial level and a national level, and different winning conditions are respectively assigned to 0.5, 1, 3 and 5.
(8) Student cadres experience label: in this embodiment, the codes of the student cadres are mainly divided into class student cadres, college student cadres and school student cadres. Specifically, the respective student cadres are assigned as follows, with 1 for school cadres, 0.5 for college cadres, and 0.2 for class cadres.
Constructing a quality model of the employing student according to the resume feature labels of the employing student, wherein the employment feature vector of the employing student is set as
The nth attribute of the mth student can be represented by a matrix (1), i.e.
Specifically, in this embodiment, for different employing students, they can view other employing students with similar characteristics to themselves, and the similarity under the same characteristic label is calculated by formula (2), that is:
in particular, the amount of the solvent to be used,
the indication of the application of the student,
and
the indication of different employing students is that,
and
respectively indicate different students applying for
And
the nth signature value, and
and
respectively, the maximum and minimum values of the nth signature value. Recruitment student
specifically, the similarity of the different student quality models is calculated through the formula (3), so that the similarity of the students applying to the employment can be obtained, and the employment information of the similar students can be obtained.
The purpose of the invention can be realized by the following technical scheme, as shown in figure 2:
an apparatus for data mining-based campus recruitment guidance, the method comprising the following modules:
the system comprises a collecting module, a judging module and a display module, wherein the collecting module is used for collecting personal resume information of an employing student, and the personal resume information comprises sex, a study, specialty, school score, language level, computer level, student cadre experience, biographical place, expected work place, practice experience and practice experience of the employing student in the period from admission to social entrance;
segmenting the personal resume information of the acquired recruitment student, and segmenting the resume part from which the time information is removed into independent semantic modules;
the preprocessing module is used for carrying out knowledge labeling word segmentation and semantic reasoning analysis on the separated semantic modules, extracting characteristics of sex, academic history, specialty, school score, language level, computer level, student cadre experience, biographical place, expected work place, practice experience and the like in the personal resume in a smooth aggregation, data generalization and standardization mode, and assigning values to one or more characteristics.
And the model module is used for constructing a quality model of the student according to each parameter in the personal resume information of the student.
Specifically, in this embodiment, in the process of assigning the value to the one or more feature tags of the personal resume information of the applicable student, the specific flow is as follows:
(1) and (3) assigning a gender label: according to the actual requirements of recruitment enterprises, different jobs have certain requirements on gender, and specifically, technical work enterprises tend to be more male, so in the embodiment, the gender is also assigned, the male assignment is 1, and the female assignment is 2.
(2) Assigning a calendar label: since the application object belongs to the college graduate, the assignment of the academic calendar is divided into doctor 5, master 3, subject 2 and major 1 according to the importance degree of the academic calendar in the embodiment.
(3) Student achievement label: in the processes of screening resumes and interviewing, the recruitment enterprise requires to check the achievement lists of the students so as to know the subject of the students to be recruited and the achievement of the students to be recruited. In this embodiment, the scores of the students are divided into five standards of fail, pass, medium, good and excellent, and for different standards, specifically, the score is assigned to 1.5 for excellent, 1 for good, 0.75 for medium, 0.5 for pass and 0 for fail.
(4) Assigning a practice label: the practice in different units and even different posts can judge the professional direction and ability of a student, and the recruitment enterprise can take a good look at the practice experience of the student. Therefore, in the embodiment, the practice units of the applicable students are extracted according to the practice experiences filled in the resumes by the applicable students. Specifically, the applicable student practice units are divided into organ and public institution, three-resource units, nationality enterprises, civil enterprises and other enterprises, and are all assigned to 1.
(5) English grade label: recruitment enterprises, particularly foreign enterprises and joint venture enterprises, can also clearly require English certificates in recruitment requirements, and particularly, in the recruitment process of domestic enterprises, the four-six English grades, the four-fourth professional English grade, the eight professional English grade, and the blessing and the Yasi are used as standards, so that the English grades of students to be recruited are assigned as follows, wherein the four-English grades are 1, the six English grades are 2, the four-professional English grades are 2, the eight professional English grades are 3, the blessing is 2, and the Yasi is 2.
(6) Computer-level tags: similar to the English rating labels, the computer rating case emphasizes the student's computer capabilities. Specifically, the first, second, third and fourth computer-level labels are respectively assigned to 1, 1.5, 2 and 3 according to the national computer-level examination.
(7) Winning case label: the prize winning condition is also an important basis for the recruitment enterprise to judge the students. Specifically, the winning categories are classified into a college level, a school level, a provincial level and a national level, and different winning conditions are respectively assigned to 0.5, 1, 3 and 5.
(8) Student cadres experience label: in this embodiment, the codes of the student cadres are mainly divided into class student cadres, college student cadres and school student cadres. Specifically, the respective student cadres are assigned as follows, with 1 for school cadres, 0.5 for college cadres, and 0.2 for class cadres.
And constructing a quality model of the employing student according to the resume feature labels of the employing students, and calculating the similarity of different employing students according to the quality model of the student in a calculation module so as to acquire employment information of the similar students.
Specifically, setting employment feature vectors of the students to
The nth attribute of the mth student can be represented by a matrix (1), i.e.
Specifically, in this embodiment, for different employing students, they can view other employing students with similar characteristics to themselves, and the similarity under the same characteristic label is calculated by formula (2), that is:
in particular, the amount of the solvent to be used,
the indication of the application of the student,
and
the indication of different employing students is that,
and
respectively indicate different students applying for
And
the nth signature value, and
and
respectively, the maximum and minimum values of the nth signature value. Recruitment student
specifically, the similarity of the different student quality models is calculated through the formula (3), so that the similarity of the students applying to the employment can be obtained, and the employment information of the similar students can be obtained.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.
Claims (8)
1. A campus recruitment guidance method based on data mining is characterized in that: the method comprises the following steps:
s1, collecting personal resume information of the employing students;
s2, performing word segmentation on the collected personal resume information of the recruitment student, and splitting the resume part without the time information into independent semantic modules;
s3, carrying out knowledge labeling word segmentation and semantic reasoning analysis on the split independent semantic modules, extracting resume data in the personal resume in a smooth aggregation, data generalization and standardization mode, classifying according to the resume data, and generating a feature label according to the classification;
s4, constructing a quality model of the student;
and S5, calculating the similarity of different employing students according to the quality models of the students, thereby obtaining the employment information of the similar students.
2. The data mining-based campus recruitment guidance method of claim 1 wherein in step S1, the biographical information comprises gender, academic history, expertise, school performance, language level, computer level, student cadre experience, biographical origin, desired job site, practice experience, and practice experience of the applicable student during the period from admission to social entry.
3. The method for data mining-based campus recruitment guidance of claim 2 wherein the one or more feature tags are assigned in step S3.
4. The method for data mining-based campus recruitment guidance of claim 1 wherein: in the steps S4 and S5, a quality model of the recruitment student is constructed according to the resume feature tags of the recruitment student, wherein the feature vector of the resume feature tag of the recruitment student is
；
Acquiring weight vector of application students for industry selection
；
And acquiring the similarity of the employing students by calculating the similarity of the applying student quality model, and acquiring the employment information of the similar students according to the similarity of the employing students.
5. A data mining-based campus recruitment guidance device is characterized in that:
the acquisition module is used for acquiring personal resume information of the employing students;
the preprocessing module is used for segmenting the personal resume information of the acquired recruitment students and splitting the resume part from which the time information is removed into independent semantic modules;
the classification module is used for carrying out knowledge labeling word segmentation and semantic reasoning analysis on the separated semantic modules, extracting resume data in the personal resume in a smooth aggregation, data generalization and standardization mode, classifying according to the resume data and generating a characteristic label according to the classification;
the model module is used for constructing a quality model of the student;
and the calculation module is used for calculating the similarity of different employing students according to the quality models of the students so as to acquire employment information of the similar students.
6. The data mining-based module for campus recruitment guidance of claim 5 wherein in the collection module the biographical information comprises gender, academic history, expertise, school achievements, language ratings, computer-level, student cadre history, biographies, desired job sites, practice history and practice history of the applicable students during the period from admission to social entry.
7. The module for data mining-based campus recruitment guidance of claim 6 wherein the one or more feature tags are assigned values in the classification module.
8. The module for data mining-based campus recruitment guidance of claim 5, wherein: in thatIn the calculation module, a quality model of the employing student is constructed according to resume feature tags of the employing student, wherein feature vectors of the resume feature tags of the employing student are
；
Acquiring weight vector of application students for industry selection
；
And acquiring the similarity of the employing students by calculating the similarity of the applying student quality model, and acquiring the employment information of the similar students according to the similarity of the employing students.
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| CN114861075A (en) * | 2022-07-11 | 2022-08-05 | 威海海洋职业学院 | Campus community recommendation method and system |
| CN117669896A (en) * | 2024-01-31 | 2024-03-08 | 长春财经学院 | Student information data acquisition and analysis system and method based on big data |
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| CN107590133A (en) * | 2017-10-24 | 2018-01-16 | 武汉理工大学 | The method and system that position vacant based on semanteme matches with job seeker resume |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114861075A (en) * | 2022-07-11 | 2022-08-05 | 威海海洋职业学院 | Campus community recommendation method and system |
| CN117669896A (en) * | 2024-01-31 | 2024-03-08 | 长春财经学院 | Student information data acquisition and analysis system and method based on big data |
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