KR102656136B1 - Artificial intelligence-based posting system - Google Patents

Abstract

The present invention relates to an artificial intelligence-based posting system that can improve user convenience and improve the efficiency of posting creation and management by automatically converting the content written by the user to fit the format of the job search site and posting it on the job search site. .

Description

Artificial intelligence-based posting system{ARTIFICIAL INTELLIGENCE-BASED POSTING SYSTEM}

The present invention relates to an artificial intelligence-based posting system. More specifically, the content written by the user is automatically converted to fit the format of the job search site and posted on the job search site, thereby improving user convenience and improving the efficiency of posting creation and management. It is about an artificial intelligence-based posting system that can improve.

Currently, brokerage sites are emerging that provide job search services so that job seekers can easily view job postings and companies can also quickly identify suitable talent.

In general, when recruiting or searching for a job on the Internet, the recruiter accesses job search sites such as Scout, Saramin, and Job Korea and enters a job advertisement that includes the desired job field, requirements, salary, working conditions, etc. . After that, when the job seeker checks the job posting and submits his or her resume, job search activities are carried out by conducting an interview between the job seeker and the recruiter to decide whether or not to hire them.

Job seekers can also access the job search site, search for posted job postings, select a company that meets the job seeker's desired conditions, and submit their resume, or provide job search information including information such as their resume, self-introduction, and career certificate. After registering, you will perform job search activities by responding to interviews with recruiters who request interviews with you.

In conventional job recruitment and job search services on the Internet, job seekers and job seekers have the inconvenience of having to log in to a job search site and then write new content each time according to the job search form and job search form provided by the job search site. There is a problem that it takes a lot of time to write an article for job search.

Korean Patent No. 10-2187861 (registered on December 1, 2020)

Therefore, the present invention was developed to solve the above-mentioned problems. The present invention improves user convenience by automatically converting the content written by the user to fit the format of the job search site and posting it on the job search site, and creates and manages postings. The purpose is to provide an artificial intelligence-based posting system that can improve efficiency.

Other objects of the present invention will become clearer through the examples described below.

The artificial intelligence-based posting system according to one aspect of the present invention collects at least one of job offer information and job search information from the user terminal and processes the job offer information or job search information according to the job offer form or job search form of the job search site. It may include a posting server that posts to a job search site.

In addition, the posting server includes an information collection unit that collects at least one of job offer information and job search information from the user terminal, a form collection unit that collects job offer forms and job search forms from a job search site, and a specific input item among job offer information and job search information. When receiving an AI phrase input event, one of the plurality of artificial intelligence phrases is automatically entered, and when an artificial intelligence phrase conversion event is received, another of the plurality of artificial intelligence phrases is stored. A written phrase recommendation unit that automatically enters one job, and an artificial intelligence conversion that creates a job posting by automatically entering job information into a job search form based on an artificial intelligence algorithm, and creates a job posting by automatically entering job information into a job search form. A search monitoring department that monitors job search sites and counts the number of job searches or job searches by hour, and posts job postings and job advertisements at the user's request, or at a specific time based on the number of job search counts or the number of job search counts. It may include a posting section that posts job openings and job postings.

In addition, the search monitoring unit calculates the job vacancy site score for the job vacancy site based on Equation 1 below, sorts the job vacancy sites according to the job vacancy site score, generates a job vacancy ranking list, and ranks the job vacancy sites. Provides a list to the user terminal,

[Equation 1]

refers to the job search site score, refers to the number of daily searches on job search sites, refers to the number of users who accessed the job search site the most, refers to the number of users who accessed the job search site the least, refers to the time when the job search site has the highest number of users accessing it, refers to the time when the job site has the lowest number of users accessing it, refers to the number of individual members registered on the job search site, refers to the number of corporate members who have joined the job search site, refers to the number of job advertisements posted on job search sites, means the number of job advertisements posted on the job search site, and the posting department can provide multi-postings for the top job search sites in the job search ranking list.

The artificial intelligence-based posting system according to an embodiment of the present invention provides the following effects.

The present invention has the effect of improving user convenience and improving the efficiency of posting creation and management by automatically converting the content written by the user to fit the format of the job search site and posting it on the job search site.

The effects of the present invention are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

Figure 1 is a schematic diagram showing an artificial intelligence-based posting system according to an embodiment of the present invention.
Figure 2 is a block diagram for explaining a posting server of an artificial intelligence-based posting system according to an embodiment of the present invention.
Figure 3 is a flow chart to explain the posting process performed in the posting server of the artificial intelligence-based posting system according to an embodiment of the present invention.

Since the present invention can be modified in various ways and can have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all transformations, equivalents, and substitutes included in the spirit and technical scope of the present invention. In describing the present invention, if it is determined that a detailed description of related known technologies may obscure the gist of the present invention, the detailed description will be omitted.

The terms used in this application are only used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Terms such as first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

Hereinafter, embodiments according to the present invention will be described in detail with reference to the accompanying drawings. In the description with reference to the accompanying drawings, identical or corresponding components will be assigned the same reference numbers regardless of the reference numerals, and duplicates thereof will be provided. Any necessary explanation will be omitted.

Hereinafter, an artificial intelligence-based posting system according to embodiments of the present invention will be described in detail with reference to FIGS. 1 to 3.

Figure 1 is a schematic diagram showing an artificial intelligence-based posting system according to an embodiment of the present invention.

Referring to FIG. 1, an artificial intelligence-based posting system may include a user terminal 100 and a posting server 200.

The user terminal 100 is a communication capable desktop computer, laptop computer, laptop, smart phone, tablet PC, mobile phone, or smart watch. (smart watch), smart glass, e-book reader, PMP (portable multimedia player), portable game console, navigation device, digital camera, DMB (digital multimedia broadcasting) player, digital voice It may include a digital audio recorder, digital audio player, digital video recorder, digital video player, PDA (Personal Digital Assistant), etc.

The user terminal 100 may be comprised of a plurality of users and may be connected to the posting server 200 through a network. The network may refer to a connection structure capable of exchanging information between the nodes of the user terminal 100 and the posting server 200, or a network connecting the user terminal 100 and the posting server 200. Networks include the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), 3G, 4G, LTE, 5G, Wi-Fi, etc. Included but not limited to this.

The user terminal 100 may access the posting server 200 through an application or website and input job offer information and job search information into the posting server 200. A user of the user terminal 100 may subscribe to the posting server 200 to use the services provided by the posting server 200.

The posting server 200 may be connected to the user terminal 100 through a network.

The posting server 200 may collect at least one of job offer information and job search information from the user terminal 100, process the job offer information or job search information according to the job offer form or job search form of the job search site, and post it on the job search site. there is.

Hereinafter, the posting server 200 will be described in detail with reference to FIG. 2.

Figure 2 is a block diagram for explaining a posting server of an artificial intelligence-based posting system according to an embodiment of the present invention.

Referring to FIG. 2, the posting server 200 includes an information collection unit 210, a form collection unit 220, a writing phrase recommendation unit 230, an artificial intelligence conversion unit 240, a search monitoring unit 250, and a posting It may include unit 260.

The information collection unit 210 may collect at least one of job offer information and job search information from the user terminal 100. Recruitment information includes the name of the recruitment business, the name of the recruitment business, the contact information of the recruitment business, the contact information of the recruitment business, the email address of the recruitment business, the address of the recruitment business, the recruitment area, the date of recruitment (required), the industry of recruitment, the rank of recruitment, and the submission of recruitment support (welfare). ), it may include at least one of the job offer salary and job description. Job search information includes job seeker name, job seeker contact information, job seeker email address, job seeker address, job seeker region, job seeker experience, job seeker input (required) date, job seeker desired industry, job seeker position, job seeker desired application surrender (welfare), job seeker desired wage, and job seeker May include at least one of the relevant introductions.

The information collection unit 210 may collect job search site subscription information from the user terminal 100. Job search site subscription information may include job search site ID, job search site subscription date, job search site activity history, etc. The information collection unit 210 may provide job search site subscription information to include the user's job search site ID when the posting unit 260, which will be described below, posts job advertisements and job advertisements to the job search site.

The information collection unit 210 may store the collected job information, job search information, and job search site subscription information in a user database. A user database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). The user database may have a data storage format that allows for free search (extraction), deletion, editing, addition, etc. of data. The user database is a relational database management system (RDBMS) such as Oracle, Infomix, Sybase, DB2, or an object-oriented database management system such as Gemston, Orion, O2, etc. It can be implemented according to the purpose of an embodiment of the present disclosure using (OODBMS) and XML Native Database such as Excelon, Tamino, and Sekaiju, and has its own function. You can have appropriate fields or elements to achieve this.

In one embodiment, the information collection unit 210 may calculate a job evaluation score based on job offer information. The job evaluation score can be calculated based on the equation below.

[Equation]

means the job evaluation score, means the weight according to the age of the job offer, means the weight according to the job offer area, means the weight according to the job position, means the weight according to the job posting date, means the weight for job application support items, means the weight for the job offer wage, refers to the number of postings of past job advertisements, may mean the number of job seekers recruited in the past ith job posting.

Here, the weight according to the recruitment age may be 10 if the recruitment condition is for all ages, 8 for those in their 60s or older, and 6 for those in their 20s to 50s. The weight according to the job offer region can be 10 if the job offer conditions are all regions, 8 if the job offer conditions are 6 or more, 6 if the job offer conditions are 3-5 jobs, and 4 if the job offer conditions are 1-2 jobs. The weight according to the job position can be 10 if the job offer is for all positions, 8 for general manager or division manager, 6 for team leader, and 6 for team member. The weight according to the recruitment date can be 10 if the recruitment condition is the entire recruitment date, 8 if the recruitment condition is scheduled to be supplied, 6 if the recruitment is in progress, and 0 if the announcement has expired. The weight for job search support items is 10 if the benefits provided at the time of hiring include meal support, snack support, lodging support, and flexible support, 8 for 3 supports, 6 for 2 supports, and 4 for 1 support. It can be. The weight for the job offer wage can be 5 if the job offer condition is the minimum hourly wage, and 10 if the job offer is above the minimum hourly wage.

Meanwhile, the conditions for weights described above are only examples, and the conditions and weight values may change depending on the situation.

For example, the weight according to the recruitment age is 8, the weight according to the recruitment region is 6, the weight according to the job position is 10, the weight according to the recruitment date is 8, the weight for job application support items is 6, and the weight according to the recruitment salary is 8. When the weight is 5 and the number of past job postings is 3, and the number of job seekers recruited from each job posting is 30, 25, and 40, the job evaluation score can be 47.5.

Recruitment evaluation scores can be provided to job seekers for reference in selecting a recruiting company.

In another embodiment, the information collection unit 210 may calculate a job search evaluation score based on job search information. The job search evaluation score can be calculated based on the equation below.

[Equation]

refers to the job search evaluation score, refers to the weight according to the age of the job seeker, refers to the weight according to the job seeker's desired region, refers to the weight according to the job seeker's rank, refers to the weight according to the job seeker's availability status, means the weight according to the job seeker's desired support items, refers to the weight of the job seeker's desired wage, refers to the number of times job seekers applied for recruitment in job postings, may refer to the number of times a job seeker applied for recruitment in a job posting and was accepted.

Here, the weight according to the job seeker's age may be 10 if the job seeker is in his 60s or older, 8 if the job seeker is in his 40s or 50s, and 6 if he is in his 20s or 30s. The weight according to the job seeker's desired region can be 10 if the job seeker's desired region is the entire region, 8 if there are 6 or more regions, 6 if there are 3-5 regions, and 4 if there are 1-2 regions. The weight according to the job seeker's rank can be 10 if the job seeker's rank is all (if not limited), 8 if the general manager or division manager, 6 if the team leader, and 6 if the job seeker is a team member. The weight according to the job seeker's availability status can be 10 if the job search condition is all input days, 8 if the input is scheduled, and 6 if the input is in progress. The weighting according to the job seeker's desired support items is 4 if the desired benefits are meal support, snack support, lodging support, and flexible system, 6 if 3 are desired, 8 if 2 are desired, and 1 if 1 benefit is desired. It can be 10 if desired. The weight for the job seeker's desired wage may be 10 if the job offer condition is the minimum hourly wage, and 5 if it is above the minimum hourly wage.

Meanwhile, the conditions for weights described above are only examples, and the conditions and weight values may change depending on the situation.

For example, the weight according to the job seeker's age is 6, the weight according to the job seeker's desired region is 4, the weight according to the job seeker's position is 10, the weight according to the job seeker's availability status is 10, and the weight according to the job seeker's desired support items is 10. If the weight is 8, the weight for the job seeker's desired wage is 10, the number of times the job seeker applied for recruitment in the job posting is 10, and the number of times the job seeker applied for recruitment in the job posting and was accepted is 3, the job search evaluation score may be 52.3. .

Job search evaluation scores can be provided to recruiting agencies for reference in selecting job seekers.

The form collection unit 220 may collect job recruitment forms and job search forms from job search sites. A job search site is an intermediary site that provides job search services and job search services to find people or jobs by posting job postings and job postings. For example, it may be Saramin, Job Korea, Worknet, Job Planet, Incruit, etc. . Recruitment forms and job search forms may be created and provided on each job search site.

The form collection unit 220 may collect job offer forms and job search forms by crawling the job search site, or may receive job offer forms and job search forms from the job search site in cooperation with the job search site.

The form collection unit 220 may store the job offer form and job search form in a form database. The form collection unit 220 may store the job search site, the job offer form, and the job search form in a form database. A form database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A form database may have a data storage format in which data can be freely searched (extracted), deleted, edited, added, etc. Form databases are relational database management systems (RDBMS) such as Oracle, Infomix, Sybase, DB2, or object-oriented database management systems such as Gemston, Orion, O2, etc. It can be implemented according to the purpose of an embodiment of the present disclosure using (OODBMS) and XML Native Database such as Excelon, Tamino, and Sekaiju, and has its own function. You can have appropriate fields or elements to achieve this.

The phrase recommendation unit 230 may store a plurality of artificial intelligence phrases that are automatically entered for specific input items among job offer information and job search information. A plurality of artificial intelligence phrases may be pre-registered by the administrator of the posting server 200, or may be automatically generated by analyzing job offer information and job search information collected from the user terminal 100 based on artificial intelligence. For example, multiple artificial intelligence phrases can be used for specific input items such as a one-line introduction to a job offer, a one-line introduction to a job offer, a detailed introduction to a job offer, a detailed introduction to a job search, etc.

When the writing phrase recommendation unit 230 receives an artificial intelligence phrase input event from a user, it can automatically input one of a plurality of artificial intelligence phrases into a specific input item. An artificial intelligence text input event can be created when a user wants text to be automatically written in a specific input item.

When the written phrase recommendation unit 230 receives an artificial intelligence phrase conversion event from the user, it can change one of the plurality of artificial intelligence phrases already entered in a specific input item and automatically input another one of the plurality of artificial intelligence phrases. . An artificial intelligence phrase conversion event can be created when a user wants to change the phrase entered in a specific input item.

In one embodiment, the writing phrase recommendation unit 230 may receive and store a plurality of artificial intelligence phrases for each user of the user terminal 100. The written phrase recommendation unit 230 stores a plurality of artificial intelligence phrases for each user, and then, upon receiving an artificial intelligence phrase input event and an artificial intelligence phrase conversion event, retrieves a plurality of artificial intelligence phrases entered by the user according to the event. You can automatically enter one of multiple artificial intelligence phrases for a specific input item.

Meanwhile, artificial intelligence (AI) refers to technology that imitates human learning ability, reasoning ability, perception ability, etc. and implements it on a computer, and includes concepts such as machine learning and symbolic logic. can do. Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the results of the analysis, and makes judgments or predictions based on the results of the learning. Additionally, technologies that mimic the functions of the human brain, such as cognition and judgment, using machine learning algorithms can also be understood as the category of artificial intelligence. For example, technical fields such as verbal understanding, visual understanding, reasoning/prediction, knowledge representation, and motion control may be included.

Machine learning can refer to the process of training a neural network model using experience processing data. Machine learning can mean that computer software improves its own data processing capabilities. A neural network model is built by modeling the correlation between data, and the correlation can be expressed by a plurality of parameters. A neural network model extracts and analyzes features from given data to derive correlations between data. Repeating this process to optimize the parameters of the neural network model can be called machine learning. For example, a neural network model can learn the mapping (correlation) between input and output for data given as input-output pairs.

Alternatively, a neural network model may learn the relationships by deriving regularities between given data even when only input data is given.

An artificial intelligence learning model or neural network model may be designed to implement the human brain structure on a computer, and may include a plurality of network nodes with weights that simulate neurons of a human neural network. A plurality of network nodes may have a connection relationship with each other by simulating the synaptic activity of neurons in which neurons exchange signals through synapses.

In an artificial intelligence learning model, multiple network nodes are located in layers of different depths and can exchange data according to convolutional connection relationships. For example, the artificial intelligence learning model may be an artificial neural network model (Artificial Neural Network), a convolution neural network (CNN), etc. As an embodiment, an artificial intelligence learning model may be machine learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning.

Machine learning algorithms for performing machine learning include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, and Convolution Neural Network. , CNN), Convolution Network, Ada-boost, Perceptron, Genetic Programming, Clustering, etc. can be used.

Among them, convolutional neural networks are a type of multilayer perceptrons designed to use minimal preprocessing. A convolutional neural network consists of one or several convolutional layers and general artificial neural network layers on top of them, and additionally utilizes weights and pooling layers. Thanks to this structure, the convolutional neural network can fully utilize input data with a two-dimensional structure. Compared to other deep learning structures, convolutional neural networks show good performance in both video and audio fields. Convolutional neural networks can also be trained via standard back propagation. Convolutional neural networks have the advantage of being easier to train and using fewer parameters than other feedforward artificial neural network techniques.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increase in the size of available training data and the availability of computational power, combined with algorithmic advances such as piecewise linear unit and dropout training, have led to significant improvements in many computer vision tasks. For extremely large data sets, such as those available for many tasks today, overfitting is not critical, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. For this purpose, distributed, scalable implementations of deep neural networks can be used.

The artificial intelligence conversion unit 240 can automatically enter job information into a job search form to create a job advertisement based on an artificial intelligence algorithm, and automatically enter job search information into a job search form to create a job advertisement.

The artificial intelligence conversion unit 240 extracts items corresponding to the job offer form from the job offer information entered by the user terminal 100, converts the extracted content to fit the job offer form, and inputs it into the job offer form.

Additionally, the artificial intelligence conversion unit 240 extracts items corresponding to the job search form from the job search information entered by the user terminal 100, converts the extracted content to suit the job search form, and inputs it into the job search form.

That is, the artificial intelligence conversion unit 240 processes the job offer information and job search information entered by the user terminal 100 according to the job offer form and job search form provided by each job search site, and automatically inputs it into the job offer form and job search form, respectively. can do.

Accordingly, even if the user only enters the job information and job search information once, the content is automatically entered according to the job search form and job search form of the job search site, so there is no need to write new content every time when filling out the job search form and job search form of the job search site. You don't have to, and you can eliminate the hassle of inputting and save time on inputting.

The search monitoring unit 250 may monitor the job search site and count the number of job searches or job searches per hour. The search monitoring unit 250 can count the number of job search searches by users accessing the job search site and extract the time for job search. The search monitoring unit 250 can count the number of job search searches by users accessing the job search site and extract the time for job search.

The search monitoring unit 250 selects the time zone with the highest number of job searches and the time zone with the highest number of job search searches, respectively, and selects the time zone with the highest number of job search searches and the time zone with the highest number of job search searches. ), you can post job postings and job postings.

Accordingly, the search monitoring unit 250 can ensure that users are very interested in the newly posted job postings and job postings because they are ranked at the top compared to other postings.

In one embodiment, the search monitoring unit 250 selects and provides the time zone with the highest number of job search searches and the time zone with the highest number of job search searches only to paid members who pay regularly among users registered with the posting server 200. can do.

The search monitoring unit 250 may monitor each of the plurality of job sites and calculate the job site score for each job site based on Equation 1 below.

[Equation 1]

refers to the job search site score, refers to the number of daily searches on job search sites, refers to the number of users who accessed the job search site the most, refers to the number of users who accessed the job search site the least, refers to the time when the job search site has the highest number of users accessing it, refers to the time when the job site has the lowest number of users accessing it, refers to the number of individual members registered on the job search site, refers to the number of corporate members who have joined the job search site, refers to the number of job advertisements posted on job search sites, may refer to the number of job advertisements posted on a job search site.

For example, when the number of daily searches on a job search site is 10000, the number of users who access the job search site the most is 1300, the number of users who accesses the job search site the least is 100, and the number of users who accesses the job search site the most is 100. The time is 10 o'clock, the time when the lowest number of users accesses the job search site is 4 o'clock, the number of individual members registered on the job search site is 5000, the number of corporate members registered on the job search site is 1000, and the number of users registered on the job search site is 1000. When the number of job advertisements posted on is 200 and the number of job advertisements posted on the job search site is 350, the job search site score may be 29.08.

The search monitoring unit 250 may generate a job search ranking list by sorting a plurality of job search sites according to the scores of each job search site, and provide the job search ranking list to the user terminal 100.

The user of the user terminal 100 may refer to the job search ranking list and determine the job posting site on which to post job postings and job postings.

The posting unit 260 posts job advertisements and job advertisements converted by the artificial intelligence conversion unit 240 on a job search site according to the user's request, or posts job advertisements at a specific time according to the number of job search counting or job search counting cases. You can post job advertisements on job search sites.

The posting unit 260 may provide multi-posting for the top job search site in the job search ranking list generated by sorting a plurality of job search sites according to the job search site score calculated by the search monitoring unit 250. Multi-posting may correspond to the function of simultaneously posting job postings and job postings to multiple job search sites. That is, the posting unit 260 simultaneously posts a plurality of job advertisements and job advertisements converted by the artificial intelligence conversion unit 240 to each job search site according to one job information and job search information input from the user terminal 100. You can post.

Figure 3 is a flow chart to explain the posting process performed in the posting server of the artificial intelligence-based posting system according to an embodiment of the present invention.

Referring to FIG. 3, the information collection unit 210 may collect at least one of job offer information and job search information from the user terminal 100 (step S310). The information collection unit 210 may collect job search site subscription information from the user terminal 100.

The information collection unit 210 may store the collected job information, job search information, and job search site subscription information in a user database.

The form collection unit 220 may collect job offer forms and job search forms from job search sites (step S320). The form collection unit 220 may collect job offer forms and job search forms by crawling the job search site, or may receive job offer forms and job search forms from the job search site in cooperation with the job search site.

The form collection unit 220 may store the job offer form and job search form in a form database.

The written phrase recommendation unit 230 stores a plurality of artificial intelligence phrases that are automatically entered for specific input items among job information and job search information, and when receiving an artificial intelligence phrase input event from a user, a plurality of artificial intelligence phrases are input for a specific input item. One of the phrases is automatically entered, and when an artificial intelligence phrase conversion event is received from the user, one of the plurality of artificial intelligence phrases already entered in a specific input item can be changed and another one of the plurality of artificial intelligence phrases can be automatically entered. There is (step S330).

The artificial intelligence conversion unit 240 can automatically enter job information into a job search form to create a job advertisement based on an artificial intelligence algorithm, and automatically enter job search information into a job search form to create a job advertisement (step S340). .

The artificial intelligence conversion unit 240 extracts items corresponding to the job offer form from the job offer information entered by the user terminal 100, converts the extracted content to fit the job offer form, and inputs it into the job offer form.

Additionally, the artificial intelligence conversion unit 240 extracts items corresponding to the job search form from the job search information entered by the user terminal 100, converts the extracted content to suit the job search form, and inputs it into the job search form.

The posting unit 260 posts job advertisements and job advertisements converted by the artificial intelligence conversion unit 240 on a job search site according to the user's request, or posts job advertisements at a specific time according to the number of job search counting or job search counting cases. A job advertisement can be posted on a job search site (step S350).

Although the present invention has been described above with reference to an embodiment of the present invention, those skilled in the art will understand the present invention in various ways without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that it can be modified and changed.

100: user terminal
200: Posting server
210: Information Collection Department
220: Form collection unit
230: Writing phrase recommendation section
240: Artificial intelligence conversion unit
250: Posting department

Claims (3)

user terminal; and
A posting server that collects at least one of job information and job search information from the user terminal, processes the job information or job search information according to the job search form or job search form of the job search site, and posts it on the job search site,
The posting server is,
an information collection unit that collects at least one of job offer information and job search information from the user terminal;
a form collection unit that collects job search forms and job search forms from the job search site;
Stores a plurality of artificial intelligence phrases that are automatically entered for specific input items among the job offer information and the job search information, automatically inputs one of the plurality of artificial intelligence phrases when an artificial intelligence phrase input event is received, and artificial intelligence a written phrase recommendation unit that automatically inputs another one of the plurality of artificial intelligence phrases when receiving a phrase conversion event;
An artificial intelligence conversion unit that automatically inputs the job information into the job search form based on an artificial intelligence algorithm to generate a job advertisement and automatically inputs the job search information into the job search form to generate a job advertisement;
a search monitoring unit that monitors the job search site and counts the number of job searches or job searches per hour; and
A posting unit that posts the job posting and the job posting according to a user's request, or posts the job posting and the job posting at a specific time according to the number of job search counting or job search counting,
The search monitoring unit,
The job search site score for the job search site is calculated based on Equation 1 below, the job search site is sorted according to the job search site score to create a job search ranking list, and the job search ranking list is created. Provided to the user terminal,
[Equation 1]

refers to the job search site score, refers to the number of daily searches on job search sites, refers to the number of users who accessed the job search site the most, refers to the number of users who accessed the job search site the least, refers to the time when the job search site has the highest number of users accessing it, refers to the time when the job site has the lowest number of users accessing it, refers to the number of individual members registered on the job search site, refers to the number of corporate members who have joined the job search site, refers to the number of job advertisements posted on job search sites, refers to the number of job advertisements posted on job search sites,
In the posting section,
An artificial intelligence-based posting system that provides multi-posting for the top job search sites in the job search ranking list.
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