CN111832039A - Employment system, equipment and medium for department of job based on machine learning - Google Patents
Employment system, equipment and medium for department of job based on machine learning Download PDFInfo
- Publication number
- CN111832039A CN111832039A CN202010459671.XA CN202010459671A CN111832039A CN 111832039 A CN111832039 A CN 111832039A CN 202010459671 A CN202010459671 A CN 202010459671A CN 111832039 A CN111832039 A CN 111832039A
- Authority
- CN
- China
- Prior art keywords
- scoring
- recruitment
- machine learning
- data
- recruitment data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000010801 machine learning Methods 0.000 title claims abstract description 44
- 230000007115 recruitment Effects 0.000 claims abstract description 107
- 238000005516 engineering process Methods 0.000 claims abstract description 12
- 238000012549 training Methods 0.000 claims abstract description 10
- 238000003860 storage Methods 0.000 claims abstract description 7
- 238000004590 computer program Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000013524 data verification Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 11
- 238000000034 method Methods 0.000 description 11
- 238000012360 testing method Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000013145 classification model Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001149 cognitive effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/105—Human resources
- G06Q10/1053—Employment or hiring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Computer Security & Cryptography (AREA)
- Human Resources & Organizations (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Computer Hardware Design (AREA)
- Bioethics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- Medical Informatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Marketing (AREA)
- Quality & Reliability (AREA)
- Computational Linguistics (AREA)
- Economics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a system, a device and a medium for recruitment of department of job based on machine learning in the field of computers, wherein the system comprises: the initialization module is used for creating a database, a scoring rule and a plurality of scoring keywords at a server side and acquiring historical recruitment data; the machine learning module is used for training the scoring keywords, the historical recruitment data and the scoring rules by the server side by utilizing a machine learning technology to obtain a scoring model; the scoring result generation module is used for the server side to acquire the latest recruitment data through the client side, and score the latest recruitment data by using the scoring model to generate a scoring result; and the scoring result storage module is used for storing the scoring result and the latest recruitment data in a database by the server and sending the scoring result to the client through an HTTP (hyper text transport protocol). The invention has the advantages that: the fairness, the rationality and the efficiency of recruitment are greatly improved.
Description
Technical Field
The invention relates to the field of computers, in particular to a system, equipment and medium for recruitment of functional departments based on machine learning.
Background
At present, the recruitment of a hospital department mostly adopts links such as stroke test screening, interview and the like to investigate an applicant, a weighted average method is applied to calculate the comprehensive score of the applicant on a stroke test and a face test section, and finally the enrollment decision of the applicant is determined according to the comprehensive score sequence. Although the method has certain scientificity, the following disadvantages exist: 1. in the interviewing link, when an examiner faces a corresponding operator, information asymmetry, a cognitive blind area and deviation often exist, and subjective deviation is easy to generate only by means of two-thirty-minute structured interviewing; 2. the interviewing link has a plurality of indexes for evaluating the comprehensive quality of the applicants, the total score of the examinees is calculated by weighted average, the comprehensive performance of the applicants is difficult to be more accurately evaluated, and the overall record evaluation of the applicants is further influenced; 3. a officer may score unfair due to bribery; 4. the test taker needs to score each candidate during interview and perform calculation such as weighted average and the like on the scores after interview, so that the efficiency is low.
Therefore, how to provide a system, a device and a medium for recruitment of department of job based on machine learning to improve fairness, rationality and efficiency of recruitment becomes a problem to be solved urgently.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a system, equipment and a medium for recruitment of functional departments based on machine learning, so as to improve the fairness, the rationality and the efficiency of recruitment.
In a first aspect, the invention provides a machine learning-based employment system for a department of employment, comprising the following modules:
the initialization module is used for creating a database, a scoring rule and a plurality of scoring keywords at a server side and acquiring historical recruitment data;
the machine learning module is used for training the scoring keywords, the historical recruitment data and the scoring rules by the server side by utilizing a machine learning technology to obtain a scoring model;
the scoring result generation module is used for the server side to acquire the latest recruitment data through the client side, and score the latest recruitment data by using the scoring model to generate a scoring result;
and the scoring result storage module is used for storing the scoring result and the latest recruitment data in a database by the server and sending the scoring result to the client through an HTTP (hyper text transport protocol).
Further, the machine learning module is specifically:
and the server side establishes a classifier by utilizing a machine learning technology, and inputs the scoring keywords, the historical recruitment data and the scoring rules into the classifier for training to obtain a scoring model.
Further, the scoring result generating module specifically includes:
the recruitment data acquisition request sending unit is used for creating a public key and a private key by the server and sending the recruitment data acquisition request and the public key to the client;
the recruitment data encryption sending unit is used for encrypting the latest recruitment data input by the public key to generate encrypted data after the client receives the recruitment data acquisition request and the public key, carrying out hash calculation on the encrypted data to generate a first hash value, and sending the encrypted data and the first hash value to the server through an HTTP (hyper text transport protocol);
the recruitment data verification unit is used for performing hash calculation on the encrypted data to generate a second hash value after the server receives the encrypted data and the first hash value, judging whether the first hash value is equal to the second hash value, and if so, entering the step S34; if not, ending the flow;
the recruitment data decryption unit is used for decrypting the encrypted data by the server side by using the private key to obtain the latest recruitment data;
and the scoring unit is used for inputting the latest recruitment data into a scoring model by the server to score and generating a scoring result.
Further, the client is a mobile phone, a tablet computer, a notebook computer or a desktop computer.
Further, the historical recruitment data comprises recruitment posts, recruitment requirements, interview questions, applicants resumes, interview recordings and interview scores; the latest recruitment data comprises recruitment posts, recruitment requirements, interview questions, applicants resumes and interview recordings.
Further, in the initialization module, the scoring rule is specifically:
setting the corresponding score of each score keyword appearing once, adding the scores of one interview to obtain the interview score, and setting the interview score to be excellent in the interval of [90,100], good in the interval of [80,90), common in the interval of [60,80) and fail in the interval of [0, 60).
In a second aspect, the present invention provides a machine learning-based job department recruitment device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the system of the first aspect when executing the program.
In a third aspect, the present invention provides a machine learning based employment medium having stored thereon a computer program which, when executed by a processor, implements the system of the first aspect.
One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:
the method comprises the steps that scoring rules and scoring keywords are created through a server, historical recruitment data are obtained, the server trains the scoring keywords, the historical recruitment data and the scoring rules by utilizing a machine learning technology to obtain a scoring model, and then scores the latest recruitment data by utilizing the scoring model, so that the subjective deviation of examinees is overcome, the comprehensive performance of applicants can be more accurately evaluated by scoring based on the scoring keywords, and finally the fairness and the rationality of recruitment are greatly improved; and the data are acquired and scored through the server, and compared with the traditional method that the examiner calculates the scores by means of weighted average and the like after interviewing, the result can be faster output, and the recruitment efficiency is greatly improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
The invention will be further described with reference to the following examples with reference to the accompanying drawings.
Fig. 1 is a schematic structural diagram of a recruitment system of a department based on machine learning according to the present invention.
Fig. 2 is a flow chart of a machine learning based employment system of the present invention.
Fig. 3 is a schematic structural diagram of a recruitment device for a department of employment based on machine learning according to the present invention.
Fig. 4 is a schematic structural diagram of a employment medium for a department of employment based on machine learning according to the present invention.
Detailed Description
The embodiment of the application realizes improvement of fairness, rationality and efficiency of recruitment by providing the system, the device and the medium for recruitment of the functional departments based on machine learning.
The technical scheme in the embodiment of the application has the following general idea: creating a scoring rule and a scoring keyword through a server, acquiring historical recruitment data, training the scoring keyword, the historical recruitment data and the scoring rule by the server by using a machine learning technology to obtain a scoring model, scoring the latest recruitment data by using the scoring model, and improving the fairness and the rationality of recruitment by using a unified scoring rule and combining the machine learning technology; and the data is acquired and scored through the server side, so that the recruitment efficiency is improved.
Example one
The embodiment provides a recruitment system for department of employment based on machine learning, as shown in fig. 1 to 2, comprising the following modules:
the initialization module is used for creating a database, a scoring rule and a plurality of scoring keywords at a server side and acquiring historical recruitment data; the scoring keywords are important scientific research projects, project responsible persons, first authors, SCI papers, intermediate-level titles, clinical experiences, strong communication capacity and the like.
The machine learning module is used for training the scoring keywords, the historical recruitment data and the scoring rules by the server side by utilizing a machine learning technology to obtain a scoring model;
the scoring result generation module is used for the server side to acquire the latest recruitment data through the client side, and score the latest recruitment data by using the scoring model to generate a scoring result;
and the scoring result storage module is used for storing the scoring result and the latest recruitment data in a database by the server and sending the scoring result to the client through an HTTP (hyper text transport protocol).
The machine learning module is specifically:
and the server side establishes a classifier by utilizing a machine learning technology, and inputs the scoring keywords, the historical recruitment data and the scoring rules into the classifier for training to obtain a scoring model. The machine learning technology can continuously update and iterate the scoring model, and further continuously improves the reasonability of the scoring result.
Classification is a very important method of data mining. The concept of classification is to learn a classification function or to construct a classification model (i.e. what we generally call Classifier) based on existing data. The function or model can map data records in the database to one of a given category and thus can be applied to data prediction. In a word, the classifier is a general term of a method for classifying samples in data mining, and includes algorithms such as decision trees, logistic regression, naive bayes, neural networks and the like.
The construction and implementation of the classifier comprises the following steps: 1. selecting samples (including positive samples and negative samples), and dividing all samples into a training sample and a test sample; 2. executing a classifier algorithm on the training samples to generate a classification model; 3. executing a classification model on the test sample to generate a prediction result; 4. and calculating necessary evaluation indexes according to the prediction result, and evaluating the performance of the classification model.
The scoring result generation module specifically comprises:
the recruitment data acquisition request sending unit is used for creating a public key and a private key by the server and sending the recruitment data acquisition request and the public key to the client; the public key and the private key are a pair of keys, the data encrypted by the public key can be decrypted only by the private key, and the data encrypted by the private key can be decrypted only by the public key;
the recruitment data encryption sending unit is used for encrypting the latest recruitment data input by the public key to generate encrypted data after the client receives the recruitment data acquisition request and the public key, carrying out hash calculation on the encrypted data to generate a first hash value, and sending the encrypted data and the first hash value to the server through an HTTP (hyper text transport protocol);
the recruitment data verification unit is used for performing hash calculation on the encrypted data to generate a second hash value after the server receives the encrypted data and the first hash value, judging whether the first hash value is equal to the second hash value, and if so, entering the step S34; if not, ending the flow; because the Hash calculation is irreversible, whether the data is distorted in the transmission process by hackers can be judged by comparing whether the results of the two Hash calculations are consistent;
by setting public and private key encryption and Hash algorithm encryption, a secondary safety protection mechanism is adopted, and the safety of data is greatly improved.
The recruitment data decryption unit is used for decrypting the encrypted data by the server side by using the private key to obtain the latest recruitment data;
and the scoring unit is used for inputting the latest recruitment data into a scoring model by the server to score and generating a scoring result.
The client is a mobile phone, a tablet computer, a notebook computer or a desktop computer.
The historical recruitment data comprises recruitment posts, recruitment requirements, interview questions, applicants resumes, interview recordings and interview scores; the latest recruitment data comprises recruitment posts, recruitment requirements, interview questions, applicants resumes and interview recordings.
The recruitment requirement is that a recruitment post is an ophthalmologist, the recruitment requirement is that a academic master researchers and above, the age is below 40 years old, the work experience is 3-5 years, the recruitment requirement has a middle-grade role and above, 1-2 SCI papers are published by a first author in nearly 2 years, the recruitment requirement has important clinical skill specialties, the recruitment requirement bears important scientific research project topics, the recruitment requirement has good doctor-patient communication ability, the work responsibility is strong, the team cooperation spirit is realized, and the like.
The interview test questions, if you assume that you are registered and then called to the basic medical and health institution to help the service, what preparation you will do? The preset answering direction is in a positive state, basic service consciousness is established, the characteristics of a service mechanism are known, own advantages are played, accumulated experience is emphasized, and the like.
The interview recording comprises self introduction, question answering records, interviewer scoring, evaluation opinions and the like.
In the initialization module, the scoring rule is specifically as follows:
setting the corresponding score of each score keyword appearing once, adding the scores of one interview to obtain the interview score, and setting the interview score to be excellent in the interval of [90,100], good in the interval of [80,90), common in the interval of [60,80) and fail in the interval of [0, 60). For example, a major research project counts 1 point, a project principal counts 2 points, a first author counts 2 points, etc.
Based on the same inventive concept, the application also provides equipment corresponding to the system in the first embodiment, which is detailed in the second embodiment.
Example two
The embodiment provides a machine learning-based job department recruitment device, as shown in fig. 3, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, any one of the first embodiment can be implemented.
Since the apparatus described in this embodiment is an apparatus used for implementing the system in the first embodiment of the present application, a person skilled in the art can understand a specific implementation manner of the apparatus in this embodiment and various variations thereof based on the system described in the first embodiment of the present application, and therefore a detailed description of how to implement the method in the embodiment of the present application by the apparatus is not provided herein. The equipment used by those skilled in the art to implement the system in the embodiments of the present application is within the scope of the present application.
Based on the same inventive concept, the application also provides a medium corresponding to the system in the first embodiment, which is detailed in the third embodiment.
EXAMPLE III
The embodiment provides a machine learning-based job department recruitment medium, as shown in fig. 4, on which a computer program is stored, and when the computer program is executed by a processor, any one of the embodiment can be implemented.
The technical scheme provided in the embodiment of the application at least has the following technical effects or advantages:
the method comprises the steps that scoring rules and scoring keywords are created through a server, historical recruitment data are obtained, the server trains the scoring keywords, the historical recruitment data and the scoring rules by utilizing a machine learning technology to obtain a scoring model, and then scores the latest recruitment data by utilizing the scoring model, so that the subjective deviation of examinees is overcome, the comprehensive performance of applicants can be more accurately evaluated by scoring based on the scoring keywords, and finally the fairness and the rationality of recruitment are greatly improved; and the data are acquired and scored through the server, and compared with the traditional method that the examiner calculates the scores by means of weighted average and the like after interviewing, the result can be faster output, and the recruitment efficiency is greatly improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
Claims (8)
1. A recruitment system for department of employment based on machine learning is characterized in that: the system comprises the following modules:
the initialization module is used for creating a database, a scoring rule and a plurality of scoring keywords at a server side and acquiring historical recruitment data;
the machine learning module is used for training the scoring keywords, the historical recruitment data and the scoring rules by the server side by utilizing a machine learning technology to obtain a scoring model;
the scoring result generation module is used for the server side to acquire the latest recruitment data through the client side, and score the latest recruitment data by using the scoring model to generate a scoring result;
and the scoring result storage module is used for storing the scoring result and the latest recruitment data in a database by the server and sending the scoring result to the client through an HTTP (hyper text transport protocol).
2. The machine learning-based employment system according to claim 1, wherein: the machine learning module is specifically:
and the server side establishes a classifier by utilizing a machine learning technology, and inputs the scoring keywords, the historical recruitment data and the scoring rules into the classifier for training to obtain a scoring model.
3. The machine learning-based employment system according to claim 1, wherein: the scoring result generation module specifically comprises:
the recruitment data acquisition request sending unit is used for creating a public key and a private key by the server and sending the recruitment data acquisition request and the public key to the client;
the recruitment data encryption sending unit is used for encrypting the latest recruitment data input by the public key to generate encrypted data after the client receives the recruitment data acquisition request and the public key, carrying out hash calculation on the encrypted data to generate a first hash value, and sending the encrypted data and the first hash value to the server through an HTTP (hyper text transport protocol);
the recruitment data verification unit is used for performing hash calculation on the encrypted data to generate a second hash value after the server receives the encrypted data and the first hash value, judging whether the first hash value is equal to the second hash value, and if so, entering the step S34; if not, ending the flow;
the recruitment data decryption unit is used for decrypting the encrypted data by the server side by using the private key to obtain the latest recruitment data;
and the scoring unit is used for inputting the latest recruitment data into a scoring model by the server to score and generating a scoring result.
4. The machine learning-based employment system according to claim 1, wherein: the client is a mobile phone, a tablet computer, a notebook computer or a desktop computer.
5. The machine learning-based employment system according to claim 1, wherein: the historical recruitment data comprises recruitment posts, recruitment requirements, interview questions, applicants resumes, interview recordings and interview scores; the latest recruitment data comprises recruitment posts, recruitment requirements, interview questions, applicants resumes and interview recordings.
6. The machine learning-based employment system according to claim 1, wherein: in the initialization module, the scoring rule is specifically as follows:
setting the corresponding score of each score keyword appearing once, adding the scores of one interview to obtain the interview score, and setting the interview score to be excellent in the interval of [90,100], good in the interval of [80,90), common in the interval of [60,80) and fail in the interval of [0, 60).
7. A machine learning based employment device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the system of any one of claims 1 to 6.
8. A machine learning based employment medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a system according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010459671.XA CN111832039A (en) | 2020-05-27 | 2020-05-27 | Employment system, equipment and medium for department of job based on machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010459671.XA CN111832039A (en) | 2020-05-27 | 2020-05-27 | Employment system, equipment and medium for department of job based on machine learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111832039A true CN111832039A (en) | 2020-10-27 |
Family
ID=72913993
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010459671.XA Pending CN111832039A (en) | 2020-05-27 | 2020-05-27 | Employment system, equipment and medium for department of job based on machine learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111832039A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002063520A1 (en) * | 2001-02-07 | 2002-08-15 | Japan Job Posting Service,Inc | Recruiting system, program and recording medium |
| CN105787639A (en) * | 2016-02-03 | 2016-07-20 | 北京云太科技有限公司 | Artificial-intelligence-based talent big data quantization precise matching method and apparatus |
| CN110489945A (en) * | 2019-07-26 | 2019-11-22 | 山东科技大学 | A kind of biographic information protection and retroactive method of divulging a secret |
| CN110866734A (en) * | 2019-11-11 | 2020-03-06 | 北京网聘咨询有限公司 | Intelligent recruitment method and system based on deep learning |
-
2020
- 2020-05-27 CN CN202010459671.XA patent/CN111832039A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002063520A1 (en) * | 2001-02-07 | 2002-08-15 | Japan Job Posting Service,Inc | Recruiting system, program and recording medium |
| CN105787639A (en) * | 2016-02-03 | 2016-07-20 | 北京云太科技有限公司 | Artificial-intelligence-based talent big data quantization precise matching method and apparatus |
| CN110489945A (en) * | 2019-07-26 | 2019-11-22 | 山东科技大学 | A kind of biographic information protection and retroactive method of divulging a secret |
| CN110866734A (en) * | 2019-11-11 | 2020-03-06 | 北京网聘咨询有限公司 | Intelligent recruitment method and system based on deep learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111402095A (en) | Method for detecting student behaviors and psychology based on homomorphic encrypted federated learning | |
| KR102105319B1 (en) | Esg based enterprise assessment device and operating method thereof | |
| Vandekerckhove et al. | A crossed random effects diffusion model for speeded semantic categorization decisions | |
| Wang et al. | Automatic classification of communication logs into implementation stages via text analysis | |
| Góes et al. | A process for human resource performance evaluation using computational intelligence: an approach using a combination of rule-based classifiers and supervised learning algorithms | |
| Shynarbek et al. | Prediction of Student’s Dropout from a University Program | |
| Lottering et al. | A model for the identification of students at risk of dropout at a university of technology | |
| Priya et al. | Student performance prediction using machine learning | |
| Zhang et al. | A probability approach to multiple criteria ABC analysis with misclassification tolerance | |
| CN111627539A (en) | System, equipment and medium for evaluating posts of functional departments | |
| Han et al. | Achieving counterfactual fairness for anomaly detection | |
| CN114219419A (en) | Concrete engineering responsibility sharing method based on block chain intelligent contract | |
| Lal et al. | IOT-based cyber security identification model through machine learning technique | |
| US10957432B2 (en) | Human resource selection based on readability of unstructured text within an individual case safety report (ICSR) and confidence of the ICSR | |
| CN111832039A (en) | Employment system, equipment and medium for department of job based on machine learning | |
| Iparraguirre-Villanueva et al. | Comparison of predictive machine learning models to predict the level of adaptability of students in online education | |
| Globa et al. | Case studies on big data | |
| Nangia et al. | A machine learning approach to identify the students at the risk of dropping out of secondary education in India | |
| US11182552B2 (en) | Routine evaluation of accuracy of a factoid pipeline and staleness of associated training data | |
| Kassler | Public health informatics, big data, advanced analytics, and emerging technology | |
| Aslam et al. | An Intelligent Model To Predict Student's Performance Using Machine Learning Techniques | |
| Nazir et al. | Examining the potential of machine learning for predicting academic achievement: A systematic review | |
| Bonet-Jover et al. | RUN-AS: a novel approach to annotate news reliability for disinformation detection | |
| RU2813438C1 (en) | System and method for identifying and using reference emotional-intellectual profile (ei-profile) by analysis groups | |
| Shih et al. | Thirty-day re-hospitalization rate prediction of diabetic patients |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201027 |