CN112182383A - Recommendation method and device for second post and computer equipment - Google Patents
Recommendation method and device for second post and computer equipment Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and provides a recommendation method and device for a second post and computer equipment, wherein the method comprises the following steps: extracting resume information in the resume of the job seeker; extracting the post information of the first post, comparing the post information of the first post with the post information in the post database, and screening according to a preset rule to obtain a plurality of related posts; calculating the score value of each related post of the job seeker; and screening out the related posts with the score values larger than the preset threshold value as second posts, and recommending the second posts to job seekers. The invention has the beneficial effects that: through obtaining job seeker's resume information and individual detail information to screen a plurality of relevant posts that correspond, rethread job seeker provides corresponding second post for job seeker at the value of appraising of every post, has improved job seeker and has screened second post efficiency, makes job seeker accurately find the second post different with current first post fast.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a second post recommendation method and device and computer equipment.
Background
With the continuous development of the internet technology, more and more job seekers choose to seek proper work through the internet, when the job seekers have the demand of changing job positions, the existing technical scheme is only to recommend other positions similar to the current position for the job seekers, the recommendation mode is too simple and rough, the recommendation is not based on resume information of the job seekers, the recommendation is only based on the current position, the personal information of the job seekers is ignored, the related information of the job seekers is not fully utilized, the recommendation accuracy and intelligence are low, and therefore a recommendation method for a second position is needed urgently.
Disclosure of Invention
The invention mainly aims to provide a method and a device for recommending a second post and computer equipment, and aims to solve the problem that the accuracy and intelligence for recommending the second post are low.
The invention provides a recommendation method for a second post, which comprises the following steps:
extracting resume information in the resume of the job seeker; the resume information comprises post information and personal detail information of a first post of the job seeker, wherein the first post is a currently engaged post of the job seeker, and the first post and the second post are different posts;
extracting the post information of the first post, comparing the post information of the first post with the post information in a post database, and screening according to a preset rule to obtain a plurality of related posts;
inputting the resume information and the post information of the plurality of relevant posts into a pre-trained post matching model to obtain the matching degree of the resume information and each relevant post in the plurality of relevant posts;
calculating the score value of the job seeker on each relevant post according to the matching degree and the skill weight information corresponding to each relevant post;
and screening the relevant post with the score value larger than a preset threshold value as the second post, and recommending the second post to the job seeker.
Further, the step of calculating the score value of the job seeker on each relevant post according to the matching degree and the weight corresponding to each relevant post comprises:
acquiring the working time, certificate information, job information and working skills of the job seeker on the first post according to the resume information and the post information of the first post;
according to the formula
Calculating the ability of each work skill of the job seeker; wherein F (xi) represents a function of the ability of the i-th work skill of the candidate as a function of the time of the practitioner,
a differential function representing the ability of the ith work skill as a function of time of said engagement, g (xi) representing the ability to calculate the ith work skill from said certification information and said on-duty information, t representing the time of engagement for said first position;
according to the formula
Calculating the score value of the relevant post; wherein G (n) represents the score value of the relevant post, wherein m is the matching degree, and wi is the weight parameter corresponding to the ith work skill.
Further, the step of extracting the post information of the first post, comparing the post information of the first post with the post information in the post database, and screening according to a preset rule to obtain a plurality of relevant posts includes:
respectively extracting a first keyword from the post information of the first post and a second keyword from the post information of the post database by using a preset text sorting algorithm;
vectorizing the first keyword and the second keyword by using a preset word vector database to obtain a first feature vector and a second feature vector;
calculating the similarity of the first feature vector and the second feature vector by using a preset similarity algorithm;
and screening the post corresponding to the post information with the similarity larger than the preset similarity value as the related post.
Further, before the step of inputting the resume information and the post information of the plurality of relevant posts into a pre-trained post matching model to obtain the matching degree between the resume information and each relevant post of the plurality of relevant posts, the method further includes:
acquiring a plurality of resumes from a resume information base, extracting corresponding resume information, acquiring the post information of the current post corresponding to each resume from the extracted resume information, and performing characterization processing on the post information of the current post and the extracted resume information;
inputting the information after the characteristic processing into a convolutional neural network model for training;
obtaining a preliminary position matching model after the convolutional neural network model is converged;
calculating a loss value of the preliminary job matching model;
judging whether the loss value is lower than a loss preset value or not;
if the loss value is lower than the loss preset value, the preliminary position matching model is determined as the position matching model;
and if the loss value is not lower than the loss preset value, adjusting the parameters of the convolutional neural network model, and continuing training the convolutional neural network model until the loss value of the trained primary position matching model is lower than the loss preset value.
Further, the step of screening out the relevant position with the score value larger than a predetermined threshold value as the second position and recommending the second position to the job seeker includes:
screening the relevant post with the score value larger than a preset threshold value as the second post;
acquiring the intention position of the job seeker in the resume information;
judging whether the intention position belongs to the second position or not;
if yes, the intention position is judged to be a matching position of the job seeker, and the matching position is marked and then sent to the job seeker.
Further, after the step of screening out the relevant position with the score value larger than the predetermined threshold value as the second position and recommending the second position to the job seeker, the method further includes:
acquiring a target post selected from the second post by the job seeker;
recommending the resume of the job seeker and the score value corresponding to the target post to a company where the target post is located.
The invention also provides a recommendation device for the second post, which comprises:
the resume information extraction module is used for extracting resume information in the resume of the job seeker; the resume information comprises post information and personal detail information of a first post of the job seeker, wherein the first post is a currently engaged post of the job seeker, and the first post and the second post are different posts;
the post information extraction module is used for extracting the post information of the first post, comparing the post information of the first post with the post information in the post database, and screening according to a preset rule to obtain a plurality of related posts;
the input module is used for inputting the resume information and the post information of the plurality of related posts into a post matching model to obtain the matching degree of the resume information and each related post in the plurality of related posts;
the scoring value calculating module is used for calculating the scoring value of the job seeker at each relevant post according to the matching degree and the skill weight information corresponding to each relevant post;
and the screening module is used for screening out the relevant post with the score value larger than a preset threshold value as the second post and recommending the second post to the job seeker.
Further, the score value calculation module includes:
a work skill information obtaining sub-module, configured to obtain, according to the resume information and the post information of the first post, the working time, the certificate information, the information of the job in the first post, and the work skills possessed by the job seeker;
a capability calculation submodule for calculating a capability according to a formula
Calculating the ability of each work skill of the job seeker; wherein F (xi) represents a function of the ability of the i-th work skill of the candidate as a function of the time of the practitioner,
a differential function representing the ability of the ith work skill as a function of time of said engagement, g (xi) representing the ability to calculate the ith work skill from said certification information and said on-duty information, t representing the time of engagement for said first position;
a score value calculation sub-module for calculating a score value according to a formula
Calculating the score value of the relevant post; wherein G (n) represents the score value of the relevant position, wherein m is the matching degreeAnd wi is a weight parameter corresponding to the ith work skill.
The invention also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any of the above methods when the processor executes the computer program.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method of any of the above.
The invention has the beneficial effects that: through obtaining job seeker's resume information and individual detail information to screen a plurality of relevant posts that correspond, rethread job seeker provides corresponding second post for job seeker at the value of appraising of every post, has improved job seeker and has screened second post efficiency, makes job seeker accurately find the second post different with current first post fast.
Drawings
Fig. 1 is a flowchart illustrating a second position recommendation method according to an embodiment of the present invention;
FIG. 2 is a block diagram illustrating a second station recommendation device according to an embodiment of the present invention;
fig. 3 is a block diagram illustrating a structure of a computer device according to an embodiment of the present application.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all directional indicators (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship between the components, the motion situation, etc. in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indicator is changed accordingly, and the connection may be a direct connection or an indirect connection.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a method for recommending a second post, including:
s1: extracting resume information in the resume of the job seeker; the resume information comprises post information and personal detail information of a first post of the job seeker, wherein the first post is a currently engaged post of the job seeker, and the first post and the second post are different posts;
s2: extracting the post information of the first post, comparing the post information of the first post with the post information in a post database, and screening according to a preset rule to obtain a plurality of related posts;
s3: inputting the resume information and the post information of the plurality of relevant posts into a pre-trained post matching model to obtain the matching degree of the resume information and each relevant post in the plurality of relevant posts;
s4: calculating the score value of the job seeker on each relevant post according to the matching degree and the skill weight information corresponding to each relevant post;
s5: and screening the relevant post with the score value larger than a preset threshold value as the second post, and recommending the second post to the job seeker.
As described in the above step S1, resume information in the resume of the job seeker is obtained, and the resume may include information such as name, academic calendar, work experience (including work time, job title, work content, etc.), project name, project role, project work description, expected salary, expected work location, etc. of the job seeker. The extraction mode can be that the resume of the job seeker is identified by a deep learning technology; the method comprises the steps of performing text analysis on information content of a resume context by combining a knowledge graph and an LSTM model, finding information points, performing information content standardization, correcting irregular and ambiguous resume content, and correcting resume information in processing modes of removing duplication, deleting punctuation marks, unifying languages, deleting irrelevant words and sentences and the like, wherein the irrelevant words and sentences comprise greetings, adjectives and the like.
As described in the above step S2, the post information of the first post, i.e. the post currently engaged by the job seeker, is then extracted, the work content of the job seeker can be analyzed from the post information of the first post, and the degree of correlation between the post in the post database and the first post can be calculated based on the post information of the first post, and the calculation may be any preset similarity algorithm, such as WMD algorithm (word mover' S distance), simhash algorithm, cosine-based similarity algorithm, and preferably: the formula is adopted:
calculating the degree of correlation of the first position with other positions, wherein X represents the characteristic set in the position information of the first position, and Y represents the characteristic set of the position information of other positions, wherein the closer the calculated value is 1, the higher the degree of correlation of the first position with the position is, the more the calculated value isA value close to 0 indicates that the first position is less relevant to that position. A correlation threshold may then be set and correlation positions greater than the correlation threshold may be screened out.
As described in step S3, since each post has different requirements for different working skills, further analysis and calculation of the content in the resume information is required, the analysis and calculation process may be calculated through a pre-trained post matching model, the post matching model is trained based on the skills and the years included in the resume information of the job seeker and the working post (which may include a plurality of working posts) of the job seeker, and the post information and the resume information of the relevant post are input into the post matching model based on the requirements for different skills of the relevant post, so that the matching degree between the job seeker and each relevant post based on the resume information can be obtained, and generally, the higher the matching degree is, the more relevant the resume information of the job seeker is related to the relevant post.
As described in step S4, since the company has different skills and different requirements for each skill, a weight may be set for each skill based on the requirements of the company for the relevant post, the score of the job seeker on each relevant post based on the resume information may be calculated, the score may be presented to the job seeker, of course, the calculation formula of the weight may be presented to the job seeker together, the job seeker may perform screening judgment, and the score may be presented to the company for the company to select a suitable job seeker. The specific calculation formula can be
Wherein
Wherein F (xi) represents the function of the i-th work skill capability of the job seeker along with the variation of the time of the job, n represents the total number of the work skills, xi represents the i-th work skill, m is the matching degree,
represents the ithA differential function in which the skill performance varies with the time of engagement, t denotes the time of engagement in the first position, g (xi) denotes the performance that is exhibited from other points, such as various certificates, participating items, etc., wi denotes the weight that the ith different skill performance corresponds to in that position, and w1+ w2+ … + wn is 1.
As described in step S5, the relevant post larger than the predetermined threshold is screened out as the second post, the predetermined threshold is a preset value, and then the second post larger than the predetermined threshold is sent to the job seeker, so that the job seeker who wants to go forward can recommend an ideal post, so that the job seeker does not deviate from the original post too much, and the job seeker can continue to be competent after changing posts.
In an embodiment, the step S4 of calculating the score value of the candidate on each of the relevant positions according to the matching degree and the weight corresponding to each of the relevant positions includes:
s401: acquiring the working time, certificate information, job information and working skills of the job seeker on the first post according to the resume information and the post information of the first post;
s402: according to the formula
Calculating the ability of each work skill of the job seeker; wherein F (xi) represents a function of the ability of the i-th work skill of the candidate as a function of the time of the practitioner,
a differential function representing the ability of the ith work skill as a function of time of said engagement, g (xi) representing the ability to calculate the ith work skill from said certification information and said on-duty information, t representing the time of engagement for said first position;
s403: according to the formula
Is calculated atThe score values of the relevant stations; and G (n) represents the score value of the relevant post, wherein m is the matching degree, wi is a weight parameter corresponding to the ith work skill, and n represents the number of the work skills.
As described in step S401, the resume information generally includes the working time, the certification information, the job information, and the work skill information of the job seeker on the first post, and then supplements and refines the information according to the post information of the first post, where the certification information is the certification information related to the first post and the second post, and the certification information not related to the first post and the second post may be ignored.
As stated in the above step S402, by the formula
It should be noted that, since different job skills of the job seeker have different degrees of growth within the same working time according to different job contents, for example, different job skills of the job seeker are also programmed, big data and block chains are made, popularization is made, after sales are made, and the like, in different fields, the job skills have different degrees of growth, and therefore, a function of variation of the ability of the corresponding job skill along with the working time is obtained according to the first post information, that is, a differential function of variation of the ability of the ith job skill along with the working time
The differential function may obtain a corresponding change relationship from the big data, or may be set by itself, and the capability of the ith work skill may be a linear function relationship or another function relationship as the time of the work varies. In addition, since different job seekers have different growth degrees and the abilities can be embodied from certificate information and job information, a function g (xi) can be set for calculation, wherein the function g (xi) comprises the corresponding abilities of various certificates, and then the two items are added to obtain the ability of the ith skill.
As described in step S403, the ability of each job skill is calculated by f (xi), multiplied by the weight wi, and then multiplied by the matching degree m to obtain the score value g (n) of the candidate in the second position, although different matching degrees exist between two different positions, and the matching degrees can be used as coefficients to be multiplied to obtain the score value g (n) of the relevant position.
In an embodiment, the step S2 of extracting the post information of the first post, comparing the post information of the first post with the post information in the post database, and obtaining a plurality of relevant posts according to the preset rule includes:
s201: respectively extracting a first keyword from the post information of the first post and a second keyword from the post information of the post database by using a preset text sorting algorithm;
s202: vectorizing the first keyword and the second keyword by using a preset word vector database to obtain a first feature vector and a second feature vector;
s203: calculating the similarity between the first feature vector and the second feature vector by using a preset similarity algorithm;
s204: and taking the post corresponding to the post information with the similarity larger than the preset similarity value as the related post.
As described in the above steps S201-S204, the preset text sorting algorithm may be a TextRank algorithm, which is improved from the PageRank algorithm of google, and is capable of extracting keywords and keyword groups of a given text and extracting key sentences of the text by using an extraction-type automatic abstract method. The first keyword and the second keyword are entity words and relation words, such as: mastering programming languages, engaging in financial tasks, exercising eclipse, processing accounting transactions, etc. Taking the professional keywords as an example for vectorization, vectorizing the professional requirements in the positions, and according to the fields corresponding to the professional requirements in the positions, such as: the automation, economics, computers and the like search the word vector corresponding to the field in a preset word vector database as a vectorized result; for example: a job positionThe "professional requirements" are: and the term "computer" is searched in a preset word vector database, and an n-dimensional vector corresponding to the term "computer" is obtained as a vectorization result of the professional requirement. If the professional requirement is as follows: and then, searching three words of automation, economics and computer and corresponding word vectors in a preset word vector database, wherein the three word vectors are used as vectorization results of professional requirements. Then reuse the formula
Calculating the similarity between the first feature vector and the second feature vector, wherein when the calculated similarity is closer to 1, the first feature vector is more similar to the second feature vector, and when the calculated similarity is closer to 0, the first feature vector is more dissimilar to the second feature vector. Then a specific preset similarity value is set in advance, the corresponding post in the post database which is larger than the preset similarity value is screened out and recorded as the relevant post.
In an embodiment, before the step S3 of inputting the resume information and the position information of the plurality of relevant positions into the position matching model to obtain the matching degree between the resume information and each of the relevant positions in the plurality of relevant positions, the method further includes:
s211: acquiring a plurality of resumes from a resume information base, extracting corresponding resume information, acquiring the post information of the current post corresponding to each resume from the extracted resume information, and performing characterization processing on the post information of the current post and the extracted resume information;
s212: inputting the information after the characteristic processing into a convolutional neural network model for training;
s213: and obtaining a preliminary position matching model after the convolutional neural network model is converged.
As described in steps S211 to S213, in this embodiment, the job and resume data are prepared first, the trained transform model training can be used to perform the characterization of the job information and resume information of the first job, then the characterized data are input into the convolutional neural network for model training, and when the training frequency reaches the set requirement, the convolutional neural network model is converged, so as to train a preliminary job matching model. After the training is completed, any position can be input into the preliminary position matching model for position matching, whether the preliminary position matching model is matched or not can be returned, and the matching degree of each information point is large according to the number of the information points in each position matching.
S214: calculating a loss value of the preliminary job matching model;
s215: judging whether the loss value is lower than a loss preset value or not;
s216: if the loss value is lower than the loss preset value, the preliminary position matching model is determined as the position matching model;
s217: and if the loss value is not lower than the loss preset value, adjusting parameters of the convolutional neural network model, and continuing training the convolutional neural network model until the loss value of the trained primary position matching model is lower than the loss preset value.
As described above in steps S214-S217, by formula
Calculating a loss value of the preliminary job matching model, wherein S is a loss value and A is a loss valueiRepresenting the ith calculated value, B, calculated by the preliminary job matching modeliIs a and AiThe corresponding ith prediction value, n represents the number of data. Then judging whether the loss value is lower than a preset loss value, wherein the preset loss value is a set value, and if the loss value is lower than the preset loss value, determining the preliminary job matching model as the job matching model; and if the loss value is not lower than the loss preset value, adjusting the parameters of the convolutional neural network model, and continuing training the convolutional neural network until a primary position matching model with the loss value lower than the loss preset value is obtained. Thereby improving the accuracy of the model.
In one embodiment, the step S5 of selecting the relevant position with the score value greater than the predetermined threshold as the second position and recommending the second position to the job seeker includes:
s501: screening the relevant post with the score value larger than a preset threshold value as the second post;
s502: acquiring the intention position of the job seeker in the resume information;
s503: judging whether the intention position belongs to the second position or not;
s504: if yes, the intention position is judged to be a matching position of the job seeker, and the intention position is sent to the job seeker in the form of the matching position.
As described in the foregoing steps S501 to S504, the intended post of the job seeker in the resume information may be obtained by semantic recognition after the resume information is obtained, and then it is detected that the intended post is in the second post that is not screened out, and if the intended post is in the second post, a company related to the intended post may be pushed to the job seeker in advance, so that the job seeker may select his own intention according to his own circumstances and may adapt to his own company. The job seeker is sent in the form of the matched position, wherein the form of the matched position is different from the recommended form of other second positions, for example, the matched position can be sent to the job seeker after being specially marked, or the matched position can be sent to the job seeker first, and then other second positions are sent to the job seeker.
In one embodiment, after the step S5 of selecting the relevant position with the score value greater than the predetermined threshold as the second position and recommending the second position to the job seeker, the method further includes:
s601: acquiring a target post selected from the second post by the job seeker;
s602: recommending the resume of the job seeker and the score value corresponding to the target post to a company where the target post is located.
As described in the foregoing steps S601-S602, after the plurality of second posts are screened for the job seeker, if the job seeker is interested in the screened second posts, the target posts selected by the job seeker are received, and at this time, the resume and the score of the job seeker can be sent to the company where the target posts are located, so that the company can know the job seeker.
Referring to fig. 2, the present invention also provides a second position recommendation apparatus, including:
the resume information extraction module 10 is used for extracting resume information in the resume of the job seeker; the resume information comprises post information and personal detail information of a first post of the job seeker, wherein the first post is a currently engaged post of the job seeker, and the first post and the second post are different posts;
the post information extraction module 20 is configured to extract the post information of the first post, compare the post information of the first post with the post information in the post database, and obtain a plurality of relevant posts by screening according to a preset rule;
an input module 30, configured to input the resume information and the post information of the multiple relevant posts into a post matching model, so as to obtain a matching degree between the resume information and each relevant post in the multiple relevant posts;
a scoring value calculating module 40, configured to calculate a scoring value of the job seeker at each relevant post according to the matching degree and the skill weight information corresponding to each relevant post;
and the screening module 50 is configured to screen out the relevant post with the score value larger than a predetermined threshold value as the second post, and recommend the second post to the job seeker.
The resume information in the resume of the job seeker is obtained, and the resume can comprise information such as the name, the academic calendar, the work experience (including work time, position name, work content and the like), the project name, the project role, the project work description, expected salary, expected work place and the like of the job seeker. The extraction mode can be that the resume of the job seeker is identified by a deep learning technology; the method comprises the steps of performing text analysis on information content of a resume context by combining a knowledge graph and an LSTM model, finding information points, performing information content standardization, correcting irregular and ambiguous resume content, and correcting resume information in processing modes of removing duplication, deleting punctuation marks, unifying languages, deleting irrelevant words and sentences and the like, wherein the irrelevant words and sentences comprise greetings, adjectives and the like.
Then extracting the position information of the first position, namely the position in which the job seeker is currently engaged, analyzing the work content of the job seeker and the skill required by the first position from the position information of the first position, and then calculating the correlation degree between the positions in the position database and the first position based on the position information of the first position, wherein the calculation mode can be any preset similarity calculation method, such as a WMD algorithm (word mover's distance), a simhash algorithm, an algorithm based on cosine similarity, preferably: the formula is adopted:
and calculating the degree of correlation of the first position and other positions, wherein X represents the characteristic set in the position information of the first position, and Y represents the characteristic set of the position information of other positions, wherein the closer to 1 the calculated value represents the higher the correlation degree of the first position and the position, and the closer to 0 the calculated value represents the lower the correlation degree of the first position and the position. A correlation threshold may then be set and correlation positions greater than the correlation threshold may be screened out.
Because each post has different requirements for different working skills, further analysis and calculation of the content in the resume information is needed, the analysis and calculation process can be calculated through a pre-trained post matching model, the post matching model is formed by training the posts (which can include a plurality of posts) of the job seeker based on the skills and the years contained in the resume information of the job seeker and the posts (which can include a plurality of posts), which is equivalent to inputting the post information and the resume information of the relevant posts into the post matching model based on the requirements of the relevant posts for different skills, so that the degree of matching between the job seeker and each relevant post based on the resume information can be obtained, and generally, the higher the degree of matching is, the more relevant the resume information of the job seeker and the relevant posts are.
Because the skills of the job seeker and the requirements of the job seeker on each skill are different in the aspect of a company, a weight can be set for each skill based on the requirements of the company on the relevant post, the score value of the job seeker on each relevant post based on the resume information is calculated, the score value can be presented to the job seeker, the calculation formula of the weight can be presented to the job seeker together for the job seeker to carry out screening judgment and the like, and the score value can be presented to the company for the company to select a proper job seeker. The specific calculation formula can be
Wherein
Wherein F (xi) represents the function of the i-th work skill capability of the job seeker along with the variation of the time of the job, n represents the total number of the work skills, xi represents the i-th work skill, m is the matching degree,
a differential function representing the ability of the ith work skill as it varies with time of the work, t represents the time of the work in the first position, g (xi) represents the ability embodied from other aspects, such as various certificates, participating items, etc., wi represents the weight corresponding to the different ith work skill in the position, and w1+ w2+ … + wn is 1.
The related post larger than the preset threshold value is screened out to serve as a second post, the preset threshold value is a preset value, the post can be adjusted according to actual conditions, then the second post larger than the preset threshold value is sent to job hunters, the job hunters who want to change rows can be helped to recommend an ideal post, the post does not deviate too much from the original post, and the job can be continued to be competent for work after the post change work is facilitated.
In one embodiment, the score value calculating module 40 includes:
a work skill information obtaining sub-module, configured to obtain, according to the resume information and the post information of the first post, the working time, the certificate information, the information of the job in the first post, and the work skills possessed by the job seeker;
a capability calculation submodule for calculating a capability according to a formula
Calculating the ability of each work skill of the job seeker; wherein F (xi) represents a function of the ability of the i-th work skill of the candidate as a function of the time of the practitioner,
a differential function representing the ability of the ith work skill as a function of time of said engagement, g (xi) representing the ability to calculate the ith work skill from said certification information and said on-duty information, t representing the time of engagement for said first position;
a score value calculation sub-module for calculating a score value according to a formula
Calculating the score value of the relevant post; and G (n) represents the score value of the relevant post, wherein m is the matching degree, wi is a weight parameter corresponding to the ith work skill, and n represents the number of the work skills.
The resume information generally comprises the working time, certificate information, job information and each work skill information of the job seeker in the first position, and then the information is supplemented and perfected according to the position information of the first position, wherein the certificate information is certificate information related to the first position and the second position, and the certificate information unrelated to the first position and the second position can be ignored.
By the formula
The ability to calculate each work skill of the job seeker should be noted that different work skills of the job seeker are in accordance with different work contentsWithin the same working time, there are different degrees of growth, for example, programming, making big data and block chains, popularizing, after-sale, etc., in different fields, the working skills have different degrees of growth, so that the variation function of the ability of the corresponding working skill along with the working time is obtained according to the first position information, that is, the differential function of the ability of the ith working skill along with the variation of the working time
The differential function may obtain a corresponding change relationship from the big data, or may be set by itself, and the capability of the ith work skill may be a linear function relationship or another function relationship as the time of the work varies. In addition, since different job seekers have different growth degrees and the abilities can be embodied from certificate information and job information, a function g (xi) can be set for calculation, wherein the function g (xi) comprises the corresponding abilities of various certificates, and then the two items are added to obtain the ability of the ith skill.
Calculating the ability of each job skill through F (xi), multiplying by the weight wi, then accumulating and multiplying by the matching degree m to obtain the score value G (n) of the job seeker in the second position, wherein the matching degrees of different positions are different, and the matching degrees can be used as coefficients to multiply to obtain the score value G (n) of the relevant position.
In one embodiment, the position information extraction module 20 includes:
the first keyword extraction submodule is used for extracting a first keyword from the post information of the first post and extracting a second keyword from the post information of the post database by using a preset text sorting algorithm;
the vectorization submodule is used for vectorizing the first keyword and the second keyword by using a preset word vector database to obtain a first feature vector and a second feature vector;
the similarity operator module is used for calculating the similarity between the first feature vector and the second feature vector by using a preset similarity algorithm;
and the related post screening submodule is used for screening the posts with the similarity greater than the preset similarity value as the related posts.
The preset text sorting algorithm can be a TextRank algorithm which is improved from a Pagerank algorithm of a webpage importance sorting algorithm of Google, and can extract keywords and key word groups of a given text and extract key sentences of the text by using an extraction type automatic abstract method. The first keyword and the second keyword are entity words and relation words, such as: mastering programming languages, engaging in financial tasks, exercising eclipse, processing accounting transactions, etc. Taking the professional keywords as an example for vectorization, vectorizing the professional requirements in the positions, and according to the fields corresponding to the professional requirements in the positions, such as: the automation, economics, computers and the like search the word vector corresponding to the field in a preset word vector database as a vectorized result; for example: the 'professional requirements' of a certain recruitment position are as follows: and the term "computer" is searched in a preset word vector database, and an n-dimensional vector corresponding to the term "computer" is obtained as a vectorization result of the professional requirement. If the professional requirement is as follows: and then, searching three words of automation, economics and computer and corresponding word vectors in a preset word vector database, wherein the three word vectors are used as vectorization results of professional requirements. Then reuse the formula
Calculating the similarity between the first feature vector and the second feature vector, wherein when the calculated similarity is closer to 1, the first feature vector is more similar to the second feature vector, and when the calculated similarity is closer to 0, the first feature vector is more dissimilar to the second feature vector. Then a specific preset similarity value is preset, and the corresponding post screen in the post database which is greater than the preset similarity value is screenedAnd selecting and recording as the relevant position.
In one embodiment, the recommendation device for the second position further includes:
the system comprises a characteristic processing module, a data processing module and a data processing module, wherein the characteristic processing module is used for acquiring a plurality of resumes from a resume information base, extracting corresponding resume information, acquiring the post information of the current post corresponding to each resume from the extracted resume information, and performing characteristic processing on the post information of the current post and the extracted resume information;
the training module is used for inputting the information after the characteristic processing into the convolutional neural network model for training;
and the convergence module is used for obtaining a preliminary position matching model after the convolution neural network model converges.
In the embodiment, data preparation of positions and resumes is firstly carried out, the trained transform model training can be carried out to characterize the position information and the resume information of the first position, then the characterized data is input into the convolutional neural network for model training, and when the training times reach the set requirement, the convolutional neural network model is converged, so that a preliminary position matching model is trained. After the training is completed, any position can be input into the preliminary position matching model for position matching, whether the preliminary position matching model is matched or not can be returned, and the matching degree of each information point is large according to the number of the information points in each position matching.
The loss value calculation module is used for calculating the loss value of the preliminary position matching model;
the loss value judging module is used for judging whether the loss value is lower than a loss preset value or not;
the confirming module is used for confirming the preliminary position matching model as the position matching model if the loss value is lower than the loss preset value;
and the parameter adjusting module is used for adjusting the parameters of the convolutional neural network model and continuing training the convolutional neural network model if the loss value is not lower than the loss preset value until the loss value of the trained primary position matching model is lower than the loss preset value.
By the formula
Calculating a loss value of the preliminary job matching model, wherein S is a loss value and A is a loss valueiRepresenting the ith calculated value, B, calculated by the preliminary job matching modeliIs a and AiThe corresponding ith prediction value, n represents the number of data. Then judging whether the loss value is lower than a preset loss value, wherein the preset loss value is a set value, and if the loss value is lower than the preset loss value, determining the preliminary job matching model as the job matching model; and if the loss value is not lower than the loss preset value, adjusting the parameters of the convolutional neural network model, and continuing training the convolutional neural network until a primary position matching model with the loss value lower than the loss preset value is obtained. Thereby improving the accuracy of the model.
In one embodiment, screening module 50 includes:
the screening submodule is used for screening the relevant post with the score value larger than a preset threshold value as the second post;
an intention position acquisition submodule, configured to acquire an intention position of the job seeker in the resume information;
the intention position judgment submodule is used for judging whether the intention position belongs to the second position or not;
and the matching post judgment sub-module is used for judging that the intention post is the matching post of the job seeker and sending the intention post to the job seeker in the form of the matching post if the intention post is the matching post of the job seeker.
The intention post of the job seeker in the resume information can be obtained through semantic recognition after the resume information is obtained, then the intention post is detected to be in the second post which is not screened out, if the intention post is in the second post, a company related to the intention post can be pushed for the job seeker in advance, and the job seeker can select own intention and is suitable for the company according to own conditions. The job seeker is sent in the form of the matched position, wherein the form of the matched position is different from the recommended form of other second positions, for example, the matched position can be sent to the job seeker after being specially marked, or the matched position can be sent to the job seeker first, and then other second positions are sent to the job seeker.
In one embodiment, the recommendation device for the second position further includes:
the target post acquisition module is used for acquiring a target post selected by the job seeker from the second post;
and the company recommendation module is used for recommending the resume of the job seeker and the score value corresponding to the target post to the company of the target post.
After the plurality of second posts are screened for the job seeker, if the job seeker is interested in the screened second posts, the target posts selected by the job seeker are received, and at the moment, the resume and the score of the job seeker can be sent to a company where the target posts are located so that the company can know about the job seeker.
The invention has the beneficial effects that: through obtaining job seeker's resume information and individual detail information to screen a plurality of relevant posts that correspond, rethread job seeker provides corresponding second post for job seeker at the value of appraising of every post, has improved job seeker and has screened second post efficiency, makes job seeker accurately find the second post different with current first post fast.
Referring to fig. 3, a computer device, which may be a server and whose internal structure may be as shown in fig. 3, is also provided in the embodiment of the present application. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer designed processor is used to provide computational and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for storing various position information and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, may implement the second position recommendation method according to any of the above embodiments.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is only a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects may be applied.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for recommending a second station as described in any of the above embodiments can be implemented.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by hardware associated with instructions of a computer program, which may be stored on a non-volatile computer-readable storage medium, and when executed, may include processes of the above embodiments of the methods. Any reference to memory, storage, database, or other medium provided herein and used in the examples may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method for recommending a second post, comprising:
extracting resume information in the resume of the job seeker; the resume information comprises post information and personal detail information of a first post of the job seeker, wherein the first post is a currently engaged post of the job seeker, and the first post and the second post are different posts;
extracting the post information of the first post, comparing the post information of the first post with the post information in a post database, and screening according to a preset rule to obtain a plurality of related posts;
inputting the resume information and the post information of the plurality of relevant posts into a pre-trained post matching model to obtain the matching degree of the resume information and each relevant post in the plurality of relevant posts;
calculating the score value of the job seeker on each relevant post according to the matching degree and the skill weight information corresponding to each relevant post;
and screening the relevant post with the score value larger than a preset threshold value as the second post, and recommending the second post to the job seeker.
2. A method as claimed in claim 1, wherein the step of calculating the value of the credit of the candidate on each of the relevant positions according to the matching degree and the corresponding weight of each of the relevant positions comprises:
acquiring the working time, certificate information, job information and working skills of the job seeker on the first post according to the resume information and the post information of the first post;
according to the formula
Calculating the ability of each work skill of the job seeker; wherein F (xi) represents a function of the ability of the i-th work skill of the candidate as a function of the time of the practitioner,
a differential function representing the ability of the ith work skill as a function of time of said engagement, g (xi) representing the ability to calculate the ith work skill from said certification information and said on-duty information, t representing the time of engagement for said first position;
according to the formula
Calculating the score value of the relevant post; and G (n) represents the score value of the relevant post, wherein m is the matching degree, wi is a weight parameter corresponding to the ith work skill, and n represents the number of the work skills.
3. A method as claimed in claim 1, wherein the step of extracting the position information of the first position, comparing the position information of the first position with the position information in the position database, and filtering according to a preset rule to obtain a plurality of relevant positions comprises:
respectively extracting a first keyword from the post information of the first post and a second keyword from the post information of a post database by using a preset text sorting algorithm;
vectorizing the first keyword and the second keyword by using a preset word vector database to obtain a first feature vector and a second feature vector;
calculating the similarity between the first feature vector and the second feature vector by using a preset similarity algorithm;
and taking the post corresponding to the post information with the similarity larger than the preset similarity value as the related post.
4. A method for recommending second position according to claim 1, wherein said step of inputting said resume information and a plurality of said positions associated with said plurality of positions into a pre-trained position matching model to obtain a degree of matching of said resume information to each of said associated positions of said plurality of associated positions further comprises:
acquiring a plurality of resumes from a resume information base, extracting corresponding resume information, acquiring the post information of the current post corresponding to each resume from the extracted resume information, and performing characterization processing on the post information of the current post and the extracted resume information;
inputting the information after the characteristic processing into a convolutional neural network model for training;
obtaining a preliminary position matching model after the convolutional neural network model is converged;
calculating a loss value of the preliminary job matching model;
judging whether the loss value is lower than a loss preset value or not;
if the loss value is lower than the loss preset value, the preliminary position matching model is determined as the position matching model;
and if the loss value is not lower than the loss preset value, adjusting parameters of the convolutional neural network model, and continuing training the convolutional neural network model until the loss value of the trained primary position matching model is lower than the loss preset value.
5. A second position recommendation method according to claim 1, wherein said step of selecting said relevant position having a score value greater than a predetermined threshold as said second position and recommending said second position to said job seeker comprises:
screening the relevant post with the score value larger than a preset threshold value as the second post;
acquiring the intention position of the job seeker in the resume information;
judging whether the intention position belongs to the second position or not;
if yes, the intention position is judged to be a matching position of the job seeker, and the matching position is marked and then sent to the job seeker.
6. A method as claimed in claim 1, wherein the step of selecting the relevant position with the score value greater than the predetermined threshold value as the second position and recommending the second position to the job seeker further comprises:
acquiring a target post selected from the second post by the job seeker;
recommending the resume of the job seeker and the score value corresponding to the target post to a company where the target post is located.
7. A second station recommendation device, comprising:
the resume information extraction module is used for extracting resume information in the resume of the job seeker; the resume information comprises post information and personal detail information of a first post of the job seeker, wherein the first post is a currently engaged post of the job seeker, and the first post and the second post are different posts;
the post information extraction module is used for extracting the post information of the first post, comparing the post information of the first post with the post information in the post database, and screening according to a preset rule to obtain a plurality of related posts;
the input module is used for inputting the resume information and the post information of the plurality of related posts into a post matching model to obtain the matching degree of the resume information and each related post in the plurality of related posts;
the scoring value calculating module is used for calculating the scoring value of the job seeker at each relevant post according to the matching degree and the skill weight information corresponding to each relevant post;
and the screening module is used for screening out the relevant post with the score value larger than a preset threshold value as the second post and recommending the second post to the job seeker.
8. The second station recommendation device according to claim 7, wherein said score value calculation module comprises:
the job skill information acquisition submodule is used for acquiring the working time, the certificate information and the on-duty information of the job seeker at the first post and the working skills of the job seeker according to the resume information and the post information of the first post;
a capability calculation submodule for calculating a capability according to a formula
Calculating the ability of each work skill of the job seeker; wherein F (xi) represents a function of the ability of the i-th work skill of the candidate as a function of the time of the practitioner,
a differential function representing the ability of the ith work skill as a function of time of said practitioner, g (xi) representing the ability to calculate the ith work skill from said certification information and said on-duty information, t representing the time of the practitioner for the first position;
a score value calculation sub-module for calculating a score value according to a formula
Calculating the score value of the relevant post; and G (n) represents the score value of the relevant post, wherein m is the matching degree, wi is a weight parameter corresponding to the ith work skill, and n represents the number of the work skills.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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| CN114925287A (en) * | 2022-07-22 | 2022-08-19 | 天津大学 | Intelligent knowledge management system and method based on big data |
| CN114925287B (en) * | 2022-07-22 | 2022-11-18 | 天津大学 | Intelligent knowledge management system and method based on big data |
| CN115526590A (en) * | 2022-09-16 | 2022-12-27 | 深圳今日人才信息科技有限公司 | Efficient human-sentry matching and re-pushing method combining expert knowledge and algorithm |
| CN115526590B (en) * | 2022-09-16 | 2023-08-04 | 深圳今日人才信息科技有限公司 | Efficient person post matching and re-pushing method combining expert knowledge and algorithm |
| CN117473251A (en) * | 2023-10-10 | 2024-01-30 | 北京华普亿方科技集团股份有限公司 | User job-seeking intention analysis method and system based on big data |
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