CN108153829A - A kind of resume appraisal procedure and device - Google Patents
A kind of resume appraisal procedure and device Download PDFInfo
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- CN108153829A CN108153829A CN201711320131.8A CN201711320131A CN108153829A CN 108153829 A CN108153829 A CN 108153829A CN 201711320131 A CN201711320131 A CN 201711320131A CN 108153829 A CN108153829 A CN 108153829A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
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Abstract
The invention discloses a kind of resume appraisal procedure, including：Obtain multiple first resumes and its first feedback data, to a part of first resume construction feature vector corresponding with the first feedback data including positive feedback action item in multiple first resumes as multiple positive samples, to another part the first resume construction feature vector corresponding with the first feedback data including negative-feedback action item in multiple first resumes as multiple negative samples, positive feedback action item represents that the first resume is associated with positive feedback action, and negative-feedback action item represents that the first resume is associated with negative-feedback action；Carry out the mapping relations between Training valuation model learning feature vector and positive feedback action item using multiple positive samples and multiple negative samples；And calculate assessment score of the resume to be assessed about positive feedback action item by assessment models.The present invention discloses a kind of resume apparatus for evaluating.Evaluation scheme through the embodiment of the present invention can provide resume to be assessed relatively accurate assessment score.
Description
Technical field
The present invention relates to internet arena, more particularly to a kind of resume appraisal procedure and device.
Background technology
In recent years, with the development of Internet technology, the interconnection networking in personnel recruitment market becomes mainstream already.It is large-scale comprehensive
It closes recruitment website (such as 51Job, the recruitment of intelligence connection) and the vertical recruitment website of internet industry (such as drag hook, Boss are directly engaged) covers greatly
Partial middle and high end job hunting crowd.In the case of job hunting group resume common numbers, how to be promoted with technology help enterprise
The efficiency of personnel recruitment work is the important issue that recruitment website promotes industrial competition.
At present, some mature technologies of this respect mainly include：
1st, resume text automatically analyzes and information extraction, is taken out from non-structured text using natural language understanding technology
Take the key message of resume, such as educational background, work unit, vocational skills, age that employment enterprise is facilitated to be searched on website or be
System is for the job requirement Auto-matching of enterprise's publication；
2nd, the Auto-matching of resume and enterprise's job description：The job requirement that user's resume and enterprise are issued is made into letter respectively
Breath extracts and calculates matching degree, and the high resume of matching degree is sent to enterprise HR, can simplify the work of resume selection；
3rd, the resume search based on keyword and setting condition：The HR actives of employment enterprise are supported according to keyword and are preset
Condition (such as educational background, age, length of service, work city) potential candidate of search.
Although above-mentioned three types of technology both contributes to the efficiency of enterprise personnel recruitment, these technologies all focus on use
Natural language processing technique matches the information in each resume according to enterprises recruit persons for jobs demand, only plays the role of primary election, and
The immobilization of screening conditions can cause leakage sieve some suitable persons, resume selection person in many cases there is still a need for resume into
It is capable to check portionwise thus limited for the help of resume selection person.
Invention content
In view of this, the embodiment of the present invention proposes a kind of resume appraisal procedure and device, can improve and resume is carried out
The accuracy of assessment provides effective reference for resume selection person.
For this purpose, the present invention proposes a kind of resume appraisal procedure, including：Obtain multiple first resumes and its first feedback coefficient
According to a part of first resume structure corresponding with the first feedback data including positive feedback action item in multiple first resumes
Feature vector is as multiple positive samples, to corresponding with the first feedback data including negative-feedback action item in multiple first resumes
Another part the first resume construction feature vector as multiple negative samples, the positive feedback action item represents the association of the first resume
It is acted in positive feedback, the negative-feedback action item represents that the first resume is associated with negative-feedback action；Use the multiple positive sample
And the mapping that the multiple negative sample comes between Training valuation model learning described eigenvector and the positive feedback action item is closed
System；And it is calculated by feature vector of the assessment models based on resume to be assessed according to the mapping relations described to be evaluated
Estimate assessment score of the resume about the positive feedback action item.
Preferably, the method further includes：Corresponding weighting coefficient is set for different positive feedback actions, and
The positive sample is adjusted according to the weighting coefficient.
Include preferably, adjusting the positive sample according to the weighting coefficient：It is each according to weighting coefficient adjustment
The sample weights of the positive sample；Or the sample copies of each positive sample is adjusted according to the weighting coefficient.
Preferably, the method further includes：The second feedback data of the resume to be assessed is obtained, according to described second
Whether feedback data includes the assessment score of positive feedback action item and the resume to be assessed, corrects the assessment models
The mapping relations learnt.
Preferably, the first resume construction feature vector is included：With first resume and it is associated with any described
Correlation between other resumes of positive feedback action builds the feature vector of the first resume as one of feature.
Preferably, the positive feedback action includes resume selection person for the first resume actions taken.
Preferably, first feedback data further includes auxiliary information, the assessment score correlations are in the auxiliary letter
Breath.
Preferably, the auxiliary information includes the first job information, the first trade information and/or feedback time information.
Preferably, the method further includes：For the more parts of resumes to be assessed, based on each resume to be assessed
The assessment score is ranked up each resume to be assessed.
The embodiment of the present invention proposes a kind of resume apparatus for evaluating simultaneously, and including processor, the processor operation is predetermined
Computer instruction to perform the resume appraisal procedure of any of the above-described embodiment.
Through the embodiment of the present invention, first resume feedback data is utilized and establishes assessment models, can pointedly learn
The mapping relations between feedback action of interest and the feature of resume are practised, for any resume to be assessed, assessment models energy
It is enough that relatively accurate assessment score is provided based on the mapping relations learnt according to the feature of the resume, it is provided for resume selection person
Strong technology auxiliary.
Description of the drawings
Fig. 1 is the schematic flow chart of one embodiment of the resume appraisal procedure of the present invention；
Fig. 2 is the schematic flow chart of another embodiment of the resume appraisal procedure of the present invention；
Fig. 3 is the schematic flow chart of one embodiment of the resume appraisal procedure of the present invention；
Fig. 4 is the schematic flow chart of another embodiment of the resume appraisal procedure of the present invention；
Fig. 5 is the schematic flow chart of one embodiment of the resume appraisal procedure of the present invention；
Fig. 6 is the schematic flow chart of another embodiment of the resume appraisal procedure of the present invention.
Specific embodiment
Various embodiments of the present invention are illustrated with reference to the accompanying drawings.
Fig. 1 is the schematic flow chart of one embodiment of the resume appraisal procedure of the present invention.
As shown in Figure 1, the resume appraisal procedure of the embodiment of the present invention includes：
S101, multiple first resumes corresponding with multiple first feedback data including the first feedback action item are obtained；
Wherein, the first feedback action item represents that the first resume is associated with the first feedback action, and the first feedback data can be
Based on resume selection person to the first feedback action that resume is made and active or the feedback data automatically provided, such as resume selection
Person thinks to need to check details after the abstract for seeing resume, then can perform check the action of resume details when or it
It is actively provided afterwards to the first feedback data of the resume or resume is checked based on resume selection person's execution by resume data platform
The action of details and automatically generate the first feedback data, in first feedback data can include represent check the first of details
Feedback action item.The job hunter that first feedback data can also be to provide resume is main after the feedback action for confirming resume selection person
It is dynamic to submit to the feedback data of resume data platform, for example, be invited to interview, by interview, by using phase etc., by resume number
Include representing such as inviting interview, the feedback data by interviewing feedback action item according to feedback generation of the platform based on job hunter.
First feedback action item is in addition to that can be associated with checking resume details, invitation interview, be moved by feedbacks such as interviews
Except the data item of work, the alternative qualified database of income, receipts for being associated with that such as resume selection person makes resume can also be
The feedback action of job hunter need not be notified by entering alternative interview library etc..
It should be noted that for same resume, a feedback action item is generally comprised in feedback data, when by
When the resume of one stage feedback action is by feedback action of the choice of next stage to be associated with the next stage, the letter
The feedback action item for the last stage being associated in the feedback data gone through just is covered by the feedback action item of new stage, therefore resume
Feedback data item in feedback data can reflect the choice stage that the resume is presently in well.
In the embodiment of the present invention, when obtaining the first resume, come using feedback action of interest as the first feedback action
Corresponding first resume of the first feedback data for the first feedback action item for including being associated with first feedback action is obtained, thus
Acquired resume is the resume in the same choice stage, such as is similarly in the stage for being checked resume details or place
In the stage for being invited to interview.
S102, as multiple positive samples Training valuation model learning institute is come to the multiple first resume construction feature vector
State the mapping relations between feature vector and the first feedback action item；
It, can be to these the first resumes after obtaining expression job hunter and being in multiple first resumes in same choice stage
In per a construction feature vector, feature vector can be made of the feature of multiple dimensions, and some of which feature for example may be used
To be between correlation between the text of the first resume and industry characteristic, the text of the first resume and recruitment person's corporate culture
Correlation, the text of the first resume and the resume of the job hunting in same industry and other job hunters in the same choice stage exist
The text of correlation, the first resume on text is with hunting for a job to same position in different industries and being in the same choice stage
The correlation of the resume of other job hunters on text etc..Here, the resume of other job hunters in the same choice stage by
The feedback data of other resumes identifies equally comprising the first feedback action item, i.e., it is anti-to be equally associated with first for other resumes
Present action item.As an example, when recruitment person's corporate culture or position demand be the job hunter of known certain overseas countries culture when,
It is just higher that job hunter's correlation in the experience of state's learning life is embodied in resume, then the value of this feature of resume is just
It can be higher；When between the text of the first resume and the resume of other job hunters in the same choice stage such as educational background,
When work experience etc. has more similarity, then the value of this feature of resume will be higher.
And so on, the feature that can largely explain the first feedback action can be built to the first resume.It is each by inciting somebody to action
Feature Mapping is to fixed dimension, and using the value of feature as the value of this dimension, in this way for the first feedback coefficient of each
A feature vector can be constructed, and based on associated first feedback of feedback action of interest according to corresponding first resume
Action item and this feature vector is determined as positive sample, multiple positive sample conducts so can be obtained by for multiple first resumes
Training sample.
After having constructed training sample, a variety of machine learning models can be chosen as assessment models on these training samples
It is trained so that assessment models can learn according to a large amount of training samples between feature vector and the first feedback action item
Mapping relations.Machine learning model such as GBDT, LogisticRegression, Factorization Machine, DNN etc..This
A little models can acquire the mapping relations between feature vector and sample identification/attribute according to a large amount of training samples.
S103, institute is calculated according to the mapping relations by feature vector of the assessment models based on resume to be assessed
State assessment score of the resume to be assessed about the first feedback action item.
After being trained to assessment models, any part resume to be assessed can be assessed with assessment models.Tool
For body, the feature vector of pattern similary with training sample can be constructed to a resume to be assessed, and resume to be assessed
Feature vector is input in assessment models, assessment models can according to the mapping relations learnt, be resume to be assessed feature to
Each feature evaluation value in amount, so as to obtain assessment score of the resume to be assessed about the first feedback action item.
The embodiment of the present invention establishes assessment models respectively by being directed to different feedback action items, can be resume selection person
It provides targetedly with reference to scoring, copes with the different demands of resume selection person, improve working efficiency.Such as check one for wishing more
The HR of a little candidate's resumes, the assessment models that can be increasingly focused on based on feedback action " checking details " and train are to candidate resume
The recommendation score provided, and for wishing the HR interviewed is unfolded as early as possible, it can increasingly focus on and face " is invited based on feedback action
The recommendation score that examination " or " passing through interview " provide candidate resume and the assessment models of training.
Through the embodiment of the present invention, first resume feedback data is utilized and establishes assessment models, can pointedly learn
The mapping relations between feedback action of interest and the feature of resume are practised, for any resume to be assessed, assessment models energy
It is enough that relatively accurate assessment score is provided based on the mapping relations learnt according to the feature of the resume, so as to effectively utilize letter
It goes through feedback data and strong technology auxiliary is provided for resume selection person, significantly reduce such as enterprise HR and line service supervisor and exist
Screen work load during resume.
Fig. 2 is the schematic flow chart of another embodiment of the resume appraisal procedure of the present invention.
As shown in Fig. 2, the method for the embodiment of the present invention includes：
S201, multiple first resumes corresponding with multiple first feedback data including the first feedback action item are obtained, it is right
Multiple first resume construction feature vectors are as multiple positive samples；
S202 obtains multiple second resumes corresponding with multiple second feedback data including the second feedback action item, to more
A second resume construction feature vector is as multiple negative samples；
S203, come between Training valuation model learning feature vector and the first feedback action item using positive sample and negative sample
Mapping relations；
S204, it is calculated and treated according to the mapping relations learnt by feature vector of the assessment models based on resume to be assessed
Assess assessment score of the resume about the first feedback action item.
Step S201 and S204 in the embodiment of the present invention is with the corresponding steps in embodiment illustrated in fig. 1, herein only to step
Rapid S202 and S203 is illustrated.
Specifically, not only being used in the embodiment of the present invention to the feature vector that the first resume is built as positive sample, go back
It has used to the feature vector that the second resume is built as negative sample, to be trained jointly to assessment models with positive sample.This
In, it is the first feedback data in order to mutually be distinguished with the feedback data of the first resume that the second resume is corresponded to the second feedback data
Can be identical with the source of the second feedback data and form, only the second feedback data fail to include the first feedback action item and
It is to include the second feedback action item, the second feedback action item represents that the second resume is associated with the second feedback action, and second is anti-
What feedback action represented is the feedback action of the previous either phase of the first feedback action.
Can be that positive sample assigns for assessment models is enabled to recognize positive sample and negative sample in the embodiment of the present invention
Value is 1 label (label), and is the label that negative sample assigns that value is 0.The value of label is not limited to 0 and 1, hereinafter will
It illustrates.
As an example, when the first feedback action is invites interview, the second feedback action can be before inviting the interview stage
The feedback action of either phase, such as check resume abstract or check resume details；When the first feedback action is to pass through interview,
Second feedback action can be by the feedback action of either phase before the interview stage, such as check resume abstract, check resume
Details invite interview.In other words, the second resume be the failure to reach the first resume reach the stage in the previous choice stage
Do not considered or think to be not belonging to after resume selection person considers job hunter's resume of fit person by resume selection person.
By regarding the feature vector of these the second resumes as negative sample and as the first resume in the embodiment of the present invention
The positive sample of feature vector is together trained assessment models, and study schedule and the study that can accelerate assessment models are accurate
Property.
Can be also different in the second feedback action on the basis of embodiment shown in Fig. 2 in some embodiments of the invention
The feedback action in stage sets different weighting coefficients, and adjusts negative sample according to the weighting coefficient of setting.For example, it is anti-to work as first
When feedback data item expression passes through interview, in difference the second feedback data item of feedback action of different phase is represented, those tables
Show and only checked resume abstract and do not checked that the feedback action items of the resume details reference value for assessment is relatively low, and
Those expressions have invited interview but have not had higher reference value for assessment by the feedback action item of interview, then may be used
Represent that its more important property is higher to set higher weighting coefficient to the second high feedback action of reference value, and to reference value
The second relatively low feedback action sets relatively low weighting coefficient and represents that its importance is relatively low, and is adjusted accordingly according to weighting coefficient
Negative sample, for example, negative sample sets different important scale designation or negative sample replicated more parts corresponding to weighting coefficient,
Etc..Through the embodiment of the present invention, the study accuracy of assessment models can be further improved.
It, can be after the third feedback data for obtaining resume to be assessed, according to the in the various embodiments described above of the present invention
It has been the assessment score that resume to be assessed provides that whether three feedback data, which include the first feedback action item and assessment models, is repaiied
The mapping relations that positive assessment models are learnt.For example, assessment models are dynamic about some scheduled feedback to a resume to be assessed
It is to think that the resume to be assessed has higher probability by resume selection to provide higher assessment score namely assessment result as item
During the respective feedback action of person, no matter whether the practical feedback action made to the resume to be assessed of resume selection person is assessment point
The targeted feedback action of number, can be first according to the third feedback data and resume to be assessed that resume to be assessed accordingly obtains
Before obtained assessment score correct the mapping relations that assessment models are learnt, this is because the feature vector one to resume structure
As be made of many a features, it is difficult to which the value for each feature of a feature vector occur is scoring upper limit value or lower limiting value
Situation.The embodiment of the present invention can be assessment models by constantly learning to assessment result and actual feedback action
Splendid training sample is provided, further improves the study accuracy of assessment models, improves assessment degree of can refer to.
In some embodiments of the invention, the first feedback data of the first resume is in addition to including representing the feedback of feedback action
Except action item, other relevant auxiliary information auxiliary can also be included and assessed so that assessment models can be based on simultaneously
Auxiliary information provides assessment score to resume to be assessed, then given assessment score can be associated with auxiliary information, meets letter
Go through the different screening requirements of screening person.
In an embodiment of the invention, the auxiliary information in the first feedback data of the first resume can include the first duty
Position information.For example, (the Ru Gehang when resume selection person is needed to specific job applicant screening job hunter resume and does not consider specific industry
The occupations such as accounting, foreground that industry is required to), the first job information can be included in the feedback information of the first handled resume,
So that assessment models used training sample when learning mapping relations is all based on the job information, can pointedly expire
Sufficient resume selection person is to selecting the demand of the specific professional person.
In an embodiment of the invention, the auxiliary information in the first feedback data of the first resume can include the first row
Industry information and the first job information so that assessment models learnt used in training sample be all based on the spy of specific industry
Job orientation position can pointedly help resume selection person to select the position fit person for having the sector experience.
In an embodiment of the invention, the auxiliary information in the first feedback data of the first resume can also include feedback
Temporal information so when choosing the first resume, can be chosen according to time range of interest so that assessment models into
Training sample used in row study is all based on the feedback data in special time period, can pointedly help resume selection
Person selects the candidate resume in special time period.
It should be noted that, although the feedback data that the first resume is described above includes showing for a variety of auxiliary informations
Example, although in the embodiment of the present invention in the feedback data of the first resume can not also include any auxiliary information or including
Auxiliary information but when choosing the first resume it is not intended that auxiliary information, it is possible thereby to assess the universality scoring of job hunter.Separately
On the one hand, it may also is that situation：The resume that resume data platform recommends resume selection person is adapted for example special
Job orientation position, therefore the first selected resume is actually to be associated with specific position, only in its first feedback data simultaneously
It does not embody.
The embodiment of the present invention can be applied to several scenes.For example, for job hunter, resume data platform can be utilized to instruct
The assessment models perfected carry out the resume scoring for oneself；For resume selection person, then can utilize oneself, enterprise or resume data
The assessment models of platform training come for for example based on text matches select come more parts of candidate resumes score, Ran Houke
With the sequence scored based on candidate resume, candidate resume of the scoring more than certain threshold value is chosen from candidate resume and carries out essence
Choosing.Resume selection person can also come with reference to other screening conditions such as candidate's work at present place, wages requirement, length of service etc.
Screening of candidates resume, and the selection result is ranked up according to the scoring of assessment models, the resume for ensureing to come front more accords with
Close the demand of resume selection person.So as to which through the embodiment of the present invention, the working efficiency of resume selection person can be greatlyd improve, drop
Low work load, while improve the property of can refer to of resume recommendation.
Fig. 3 is the schematic flow chart of one embodiment of the resume appraisal procedure of the present invention.
As shown in figure 3, the resume appraisal procedure of the embodiment of the present invention includes：
S301, multiple first resumes and its first feedback data are obtained；
S302 is to corresponding with the first feedback data including positive feedback action item a part of in multiple first resumes
One resume construction feature vector is as multiple positive samples；
S303, to another part corresponding with the first feedback data including negative-feedback action item in multiple first resumes
First resume construction feature vector is as multiple negative samples；
S304, come Training valuation model learning feature vector and positive feedback action using multiple positive samples and multiple negative samples
Mapping relations between；And
S305, resume to be assessed is calculated according to mapping relations by feature vector of the assessment models based on resume to be assessed
Assessment score about positive feedback action item.
In the embodiment of the present invention shown in Fig. 3, S304-S305 is similar with the S203-S204 of embodiment illustrated in fig. 2, only
Assessment models feedback action item of interest is different, and since feedback action item of interest is different, for the structure of training sample
It is also different to build mode, it is specifically described below.
In general, in embodiments of the present invention, it pays close attention to unlike in Fig. 1-embodiment illustrated in fig. 2 and is associated with resume
Some specific feedback action, but two classes will be divided into the associated all feedback actions of resume, one kind is moved for positive feedback
Make, one kind is acted for negative-feedback.Wherein, positive feedback action can be checked including such as resume selection person to what the first resume was made
Resume details invite and interview, approve that job hunter is the action of fit person in respective degrees by interview etc.；Negative-feedback acts
For such as resume selection person to the first resume make check resume abstract action, due to but fail to cause resume selection person detailed
Go through the interest for seeing complete resume, it is believed that the condition of job hunter fails to comply with the minimum requirements of resume selection person.
Therefore, when choosing the first resume in S301, can not have to whether include some according in its first feedback data
Feedback action item determines whether it is satisfactory resume, but can obtain phase according to the required total amount of training sample
Answer the resume with feedback data of quantity, then in S302-S303 by these first resumes according to its first feedback data
Include represent positive feedback action positive feedback action item or represent negative-feedback action negative-feedback action item by this
A little first resumes are divided into the first resume of two parts, to wherein correspond to positive feedback action a part the first resume construction feature to
Amount is used as multiple positive samples, to wherein corresponding to another part the first resume construction feature vector of negative-feedback action as multiple negative
Sample, then in S304 using these multiple positive samples and multiple negative samples come Training valuation model learning feature vector with it is positive and negative
The mapping relations between action item are presented, and assessment models are inputted for resume construction feature vector to be assessed in S305, are passed through
Feature vector of the assessment models based on resume to be assessed calculates resume to be assessed about positive and negative according to the mapping relations learnt
Present the assessment score of action item.
Structure about the feature vector to resume and the training to assessment models are similar with previous embodiment, such as can
With related between other resumes of any positive feedback action to being associated with based on the first resume of positive feedback action is associated with
Property as one of feature build the feature vector of the first resume, omit illustrate herein.
In the embodiment of the present invention, since feedback action is divided into two classes in the structure of training sample, i.e. positive feedback is moved
Make and negative-feedback acts, thus the assessment models trained are that the study of mapping relations is carried out for all positive feedbacks action, energy
The feature for enough considering the associated resume of positive feedback action with different phase carries out comprehensive score for resume to be assessed, so as to
It is provided for resume selection person based on the reference value considered comprehensively.
, can also be such similar to previous embodiment in some embodiments of the invention, for different positive feedbacks action settings
Different weighting coefficients, and positive sample is adjusted according to the weighting coefficient of setting.For example, in the positive feedback action for representing different phase
Different positive feedback data item in, the positive feedback action items of the resume details reference value for assessment has been checked in those expressions
It is relatively low, and interview has been invited in those expressions or has passed through the positive feedback action item interviewed for assessment with higher
Reference value, then positive feedback action that can be high to reference value set higher weighting coefficient and represent that its more important property is higher,
And the positive feedback action relatively low to reference value sets relatively low weighting coefficient and represents that its importance is relatively low, and can be according to weighting
Coefficient adjusts corresponding negative sample.Through the embodiment of the present invention, the study accuracy of assessment models can be further improved.
It, can if choosing the machine learning model that label is supported to be real number, such as GBDT about the utilization of weighting coefficient
Think sample weights of each positive sample setting corresponding to weighting coefficient, such as be when the original of positive sample marks, the positive sample
When weighting coefficient is 5, the former label 1 of the positive sample can be made to be multiplied by the 5 new mark as the positive sample obtained after weighting coefficient 5
Note.It, can if choosing the machine learning model that only support 0,1 is label, such as logistic regression (Logistic Regression)
As soon as the number of the positive sample is replicated with the value according to weighting coefficient corresponding with positive sample, for example weighting coefficient is incited somebody to action for 10
The positive sample replicates 10 times.Both methods can allow assessment models more to pay attention to important positive feedback during study
Data, if future, it is possible that obtaining the higher feedback of importance, the assessment of acquisition divided resume when resume is recommended
Number will be higher.
It similarly, can also be after the second feedback data for obtaining resume to be assessed, according to second in the embodiment of the present invention
Whether feedback data repaiies including positive feedback action item and assessment models for the assessment score provided of resume to be assessed
The mapping relations that positive assessment models are learnt.
Similarly, it can also include in the first feedback data of the first resume in the embodiment of the present invention or not include auxiliary letter
Breath such as includes auxiliary information, then the assessment score correlations that assessment models provide are in auxiliary information.Here auxiliary information can also wrap
Include the first job information, the first trade information and/or feedback time information etc..
Similarly, the assessment models in the embodiment of the present invention can be used for the more parts of candidate resumes point to resume selection person
Assessment score is not provided, and the sequence that resume selection person can be scored based on candidate resume chooses scoring one from candidate resume
Determine candidate resume more than threshold value and carry out selected, the working efficiency of raising resume selection person.
Fig. 4 is the schematic flow chart of another embodiment of the resume appraisal procedure of the present invention.
As shown in figure 4, the resume appraisal procedure of the embodiment of the present invention includes：
S401, multiple first resumes corresponding with multiple first feedback data including the first feedback action item are obtained, it is right
Each first resume construction feature vector is as negative sample；
S402, multiple second resumes corresponding with multiple second feedback data including the second feedback action item are obtained, it is right
Each second resume construction feature vector is as positive sample；
S403, carry out Training valuation model learning described eigenvector using positive sample and negative sample and the described second feedback is dynamic
Make the mapping relations between item；
S404, institute is calculated according to the mapping relations by feature vector of the assessment models based on resume to be assessed
State the probability that resume to be assessed is associated with the second feedback action in the case where being associated with the first feedback action.
Different from previous embodiment, it is anti-to represent that the first resume is associated with first for the first feedback action item in the embodiment of the present invention
Feedback acts, and the second feedback action item represents that the second resume is associated with the second feedback action, and the first feedback action is defined as second
The feedback of the feedback action of the previous stage of feedback action, i.e. the first feedback action and the second feedback action for two adjacent phases
Action, in other words, the embodiment of the present invention be concerned with relationship between the feedback action of two adjacent phases or further and
Speech, it is contemplated that by the possibility of the feedback action of next stage or generally after the feedback action by previous stage
Rate, thus, the embodiment of the present invention is different from previous embodiment on the structure of training sample.
Specifically, in order to learn the relationship between the feedback action of two adjacent phases, it is necessary first to be determined as pass
Target the latter half feedback action is gazed at, such as it is contemplated that resume selection person interviews this instead for the invitation that resume is made
Feedback acts, then will invite interview as the second feedback action, using as invite the previous stage interviewed check resume details this
One feedback action searches the resume with feedback data as the first feedback action, therefrom obtain with including check details this
Corresponding first resume of feedback data of one feedback action item and the feedback data pair that this feedback action item is interviewed including inviting
The second resume answered, to the feature vector that the first resume is built as negative sample, the feature vector conduct to the second resume structure
These positive samples and negative sample are trained assessment models as training sample by positive sample, with learning characteristic vector with making
The mapping relations between the second feedback action item to pay close attention to target.After training assessment models, for being already associated with first
The resume to be assessed of feedback action can input assessment models with construction feature vector, obtain the resume to be assessed and be associated with second
The probability of feedback action.Here probability is also a kind of assessment score, such as if commented in the present embodiment for another angle
The scoring bound ranging from 0-100 of model is estimated, when assessment models provide 60 points of scoring to resume, it is believed that should
The probability that resume is associated with the second feedback action on the basis of the first feedback action is associated with is 60%.
The training method of the building mode of the feature vector of resume and assessment models and aforementioned implementation in the embodiment of the present invention
Example is similar, for example, using the first resume or the second resume be associated with the second feedback action other resumes between correlation as
One of feature builds the feature vector of the first resume or the second resume.Also there are one with previous embodiment for the embodiment of the present invention
Difference is not consider the weighting coefficient of sample, without the resume feedback data for considering whole, such as when training is invited based on interview
Please this feedback action assessment models when, it is only necessary to consider that including expression checks the feedback of this feedback data item of resume details
Data and and both resumes including representing to invite the feedback data for interviewing this feedback data item, the former is for designing negative sample
This, the latter trains assessment models for designing positive sample accordingly.According to such setting, the assessment come is trained
Model can predict " in the case where resume selection person has checked resume details, send out interview and invite " for resume to be assessed
Probability.
It therefore, through the embodiment of the present invention, can be according to resume selection person to the stage sexual act of candidate resume and dynamically
Provide the recommendation of next stage, as when from model recommend be contemplated that check details candidate resume in select predetermined quantity
After the candidate resume of interview is invited in preparation, model can provide corresponding job hunter respectively for resumes of these plan invitation interviews and lead to
The probability of interview is crossed, and can be based on sequence of the given probability value by the progress of these resumes from high to low, so as to be resume
Screening person provides accurate reference value, removes the work load that a part is further screened from, improves the work of resume selection person
Make efficiency, the situation of assessment models is slightly depended on suitable for resume selection person.
It similarly, can also be after the third feedback data for obtaining resume to be assessed, according to third in the embodiment of the present invention
Feedback data whether include as concern target the second feedback action item and according to assessment models it is previously to be assessed to this
What resume evaluated is associated with the probability of the second feedback action item, to correct the mapping relations that assessment models are learnt.
Similarly, it can also include in the first feedback data of the first resume in the embodiment of the present invention or not include auxiliary letter
Breath such as includes auxiliary information, then the assessment probabilistic correlation that assessment models provide is in auxiliary information.Here auxiliary information can also wrap
Include the first job information, the first trade information and/or feedback time information etc..
Fig. 5 is the schematic flow chart of one embodiment of the resume appraisal procedure of the present invention, and the embodiment of the present invention is base
In the variant embodiment of embodiment illustrated in fig. 4.
As shown in figure 5, the appraisal procedure of the embodiment of the present invention includes：
S501, choose multigroup resume, every group of resume include with including a kind of multiple feedback data pair of feedback action item
A part of resume and another part resume corresponding with multiple feedback data including another feedback action item answered；
S502, for every group of resume, to a part of resume, each construction feature vector is as negative sample, to another part resume
Each construction feature vector is as positive sample；
S503, come using negative sample and positive sample Training valuation model learning feature vector and another feedback action item it
Between mapping relations；
S504, multiple assessment models based on multigroup resume establish fusion assessment models；
S505, by the fusion assessment models, the feature vector based on resume to be assessed calculates the letter to be assessed
Go through the probability for being associated with object feedback action item.
In embodiments of the present invention, the feedback action two of multiple successive stages that resume may be made for resume selection person
Assessment models are built between two.For each group of resume, a kind of above-mentioned feedback action item represent feedback action be on
The feedback action of the previous stage for the feedback action that another feedback action item stated represents, and for multigroup resume and
Speech, what above-mentioned a kind of feedback action item in one group of resume represented be these successive stages feedback action in the initial period
Feedback action, separately have that the above-mentioned another feedback action item in one group of resume represents be these successive stages feedback action in
The feedback action of final stage, the feedback action of the final stage correspond to above-mentioned object feedback action item.Meanwhile in addition to representing
Except the feedback action item of the feedback action of initial period and the feedback action of expression final stage, the others of different resume groups
There is serial relation two-by-two between the feedback action that feedback action item represents.
In the embodiment of the present invention, in S501, when choosing every group of resume, it will include representing to be belonging respectively to two continuous ranks
Two kinds of resumes of the feedback action item of the feedback action of section are assigned in same group, a part of letter so as to include in this group of resume
The feedback data gone through includes the feedback action in previous stage in two successive stages, another part letter that this group of resume includes
The feedback data gone through includes the feedback action of latter stage in the two successive stages.
In S502, for every group of resume, to being associated with the feedback action in the previous stage in two successive stages
A part of resume construction feature vector is as negative sample, to being associated with the feedback action of the latter stage in two successive stages
Another part resume construction feature vector as positive sample, to a part of resume or another part resume construction feature to
During amount, can by with a part of resume or another part resume respectively be associated with above-mentioned another feedback action item its
Correlation between his resume builds each feature vector as one of feature.
In S503, correspond to the assessment models of this group of resume with corresponding positive and negative sample training for every group of resume, learn
Mapping relations between the feedback action item of the feature vector for practising resume and the feedback action for representing the latter half.
Then, it as the key of the embodiment of the present invention, needs that the trained each assessment models of every group of resume will be passed through
Certain algorithm is merged, and establishes fusion assessment models, and then resume to be assessed is carried out about mesh with fusion assessment models
Mark the probability assessment of feedback action.
For example, it can be directed to check that resume group training of the resume abstract for the latter half feedback action checks that details are assessed
Model F1 invites interview assessment models F2, and be directed to for resume group of the interview for the latter half feedback action to be invited to train
To be trained by resume group of the interview for the latter half feedback action by interviewing assessment models F3.Reference embodiment illustrated in fig. 4,
Each assessment models in these assessment models F1, F2 and F3, which can be independently operated, is respectively used to prediction for example to job orientation
In the case of position, resume selection person clicks and checks the probability of resume complete information, resume in the case where seeing resume abstract
The probability that screening person initiates the probability of interview invitation after resume details are checked and candidate passes through interview.Shown in Fig. 5
In the embodiment of the present invention, after being merged to these three assessment models F1, F2 and F3, fusion assessment models F (F1 can be generated
(x), F2 (x), F3 (x)), wherein x is the feature vector built for resume.
In embodiments of the present invention, the whole acceptance probability conduct for candidate resume can be provided to resume selection person
The whole recommendation of whole feedback stages such as ought conform to the principle of simplicity to go through data platform or other approach recommend the candidate resume of predetermined quantity
Afterwards, fusion assessment models can directly give probability of the corresponding job hunter by interview, and can be based on by candidate resume to every part
These resumes are carried out sequence from high to low by given probability value, are had so as to be provided for resume selection person compared with high reference
The suggested design of value eliminates the most work burden of resume selection person, greatly improves the work of resume selection person
Make efficiency, the situation of assessment models is highly dependent on suitable for resume selection person.In fact, sample is trained through a large amount of priori in model
After the training of this and posteriority training sample, assessment accuracy will be greatly improved, thus be relied on even for slight before
For the resume selection person of assessment models, it is also contemplated that height relies on assessment models to mitigate work load.
In embodiments of the present invention, to every part of resume in every group of resume can in a like fashion construction feature vector,
So that the feature vector of each resume includes the feature of identical dimensional.
In the embodiment of the present invention, by multiple assessment models merge into fusion assessment models amalgamation mode can there are many,
For example multiple assessment models are combined as merging assessment models or be passed through by the way that each assessment models are weighted with average mode
Average mode is weighted after taking the logarithm to each assessment models again, multiple assessment models are combined as fusion assessment models, etc.
Deng.
It similarly, can also be after the feedback data for obtaining resume to be assessed, according to the feedback coefficient in the embodiment of the present invention
According to whether including object feedback action item and according to the association that had previously been evaluated to the resume to be assessed of fusion assessment models
In the probability of object feedback action item, to correct the mapping relations that assessment models are learnt.
Similarly, it can also include or not wrap in the feedback data of each resume in the embodiment of the present invention in each group resume
Auxiliary information is included, such as includes auxiliary information, then the assessment probabilistic correlation that assessment models provide is in auxiliary information.Here auxiliary information
It can also include the first job information, the first trade information and/or feedback time information etc..
In addition, in embodiments of the present invention, screening person can also be included in the feedback data of each resume of each group resume
Information since the training sample of use is based on particular screen person and is built, so trained assessment models as auxiliary information
It, can be using candidate letter of the higher accuracy to meet particular screen person's requirement by as the exclusive assessment models of particular screen person
It goes through and provides higher assessed value, and the resume for the requirement of another screening person according to may obtain relatively low assessed value.
Below with reference to Fig. 6 to based on screening person's information and the scheme of Training valuation model is described in detail.
Fig. 6 is the schematic flow chart of another embodiment of the resume appraisal procedure of the present invention.
As shown in fig. 6, the resume appraisal procedure of the embodiment of the present invention can include：
S601, acquisition are corresponding with multiple first feedback data including the first feedback action item and first screening person's information
Multiple first resumes；
S602, as multiple positive samples Training valuation model learning institute is come to the multiple first resume construction feature vector
State the mapping relations between feature vector and the first feedback action item；And
S603, institute is calculated according to the mapping relations by feature vector of the assessment models based on resume to be assessed
State assessment score of the resume to be assessed about the first feedback action item.
This embodiment of the invention is the modification of embodiment illustrated in fig. 1.On the basis of embodiment shown in Fig. 1, the present invention is real
First screening person's information that example further considers that the first feedback data of the first resume includes is applied, that is, the first feedback action
The first feedback action represented by is the action made by the first screening person to the first resume.In the embodiment of the present invention, in addition to
Except the resume of feedback action that the selection of first resume makes resume based on same screening person, other processing procedures and Fig. 1
Illustrated embodiment is essentially identical.
Through the embodiment of the present invention, it is different resumes according to the different demands of different resume selection persons or personal like
Screening person trains different assessment models, these different assessment models may provide different recommendations to same candidate resume
Value, the personalized of candidate resume can be pushed away to obtain according to personalized demand by being achieved in different resume selection persons
It recommends.
For example, in practical application, same position can have multidigit resume selection person sometimes, such as similarly for IOS high
Grade engineer may have the supervisor from two different departments to screen resume, and two people may have the preference of candidate
Certain difference is more prone to the person of You Guo major companies resume than being responsible for if any one.Theoretically, it can be this two resume selections
Person establishes a position respectively, but from the angle of enterprise HR, position division is carefully unfavorable for very much managing, so merging each business department
It is also common way in the demand a to position of door.And the embodiment of the present invention is due to base when for resume construction feature vector
In the personal data of specific resume selection person, it is possible to realize personalized recommendation, that is to say, that check IOS for two
The operating officer of senior engineer's position candidate's resume, the assessment models of corresponding training will recommend different candidate's letters for it
It goes through, this is that the existing resume based on text matches recommends method that can not reach.
In embodiments of the present invention, to the structure of the feature vector of the first resume, also there are some with above-mentioned other embodiment
Difference.Specifically, when to the first resume construction feature vector, it may be considered that with the first resume and be associated with same screening person
The first feedback action other resumes between correlation the feature vector of each first resume is built as one of feature, also
It is contemplated that correlation using between other resumes of the first feedback action of the first resume screening person different from being associated with as
One of feature builds the feature vector of each first resume.It is related to screening person by considering when for resume construction feature vector
Feature, the personal preference of particular screen person can be captured, consider the personal preference of screening person when scoring for candidate resume
Inside, personalized accurate recommendation is provided for specific resume selection person.
In an embodiment of the invention, with previous embodiment similarly, it can also be created for the training of assessment models negative
Sample.Such as it can obtain corresponding with multiple second feedback data including the second feedback action item and first screening person's information
Multiple second resumes carry out the Training valuation together with positive sample to multiple second resume construction feature vector as multiple negative samples
Model learning mapping relations.Wherein, the second feedback action item represents that the second resume is associated with the second feedback action, and the second feedback is dynamic
Work can be that feedback action (embodiment shown in Figure 4), the first feedback action of the previous stage of the first feedback action are previous
The feedback action (embodiment shown in Figure 2) in each stage or can also be the anti-of attribute opposite with the first feedback action
Feedback action (such as the first feedback action and the second feedback action are respectively positive and negative feedback action, referring specifically to embodiment illustrated in fig. 3).
It similarly, can also be after the third feedback data for obtaining resume to be assessed, according to third in the embodiment of the present invention
Feedback data whether correct by the assessed value that has provided for resume to be assessed including the first feedback action item and assessment models
The mapping relations that assessment models are learnt.
Similarly, it can also include in the first feedback data of the first resume in the embodiment of the present invention or not include auxiliary letter
Breath such as includes auxiliary information, then the assessment score correlations that assessment models provide are in auxiliary information.Here auxiliary information can also wrap
Include the first job information, the first trade information and/or feedback time information etc..
Similarly, the assessment models in the embodiment of the present invention can be used for the more parts of candidate resumes point to resume selection person
Do not provide assessment score or the assessment assessed values such as probability, resume selection person can the sequence based on candidate resume assessed value, from time
The candidate resume chosen and scored more than certain threshold value in resume is selected to carry out selected, improve the working efficiency of resume selection person.
System is recruited to an exemplary cloud as the feedback data acquisition modes of resume in the embodiment of the present invention below
It illustrates, it should be noted that example that the present invention is not limited thereto, other feedback data acquisition modes such as job hunter or active
Submission feedbacks data to resume data platform or resume selection person owned enterprise and builds resume inventory simultaneously in resume data platform
It voluntarily records feedback action data or voluntarily builds resume inventory after downloading resume data and voluntarily record feedback action data
It is also within the scope of the invention.
Different with traditional enterprises recruitment system, cloud recruitment system is a kind of SAAS (Software asservice)
Platform.This platform is developed and is safeguarded by third party, can access internet as all enterprises services for having recruitment regulatory requirement.
As user, using the enterprise of the platform without purchase, the system is disposed and safeguarded, it is only necessary to according to number of users and use
Time pays.For this kind of cloud recruitment system there are two types of the mode for establishing and safeguarding personnel resume library, the first situation is this
The cloud recruitment system of sample is developed in itself by Large-Scale Interconnected net recruitment website, can directly share the personnel resume money of its parent company
Source；The second situation is independent cloud recruitment system, enterprise side mandate system to be used is needed to be downloaded using enterprise account big
On type internet recruitment website the relevant all resumes of position are issued with enterprise.In the latter case, once cloud recruits system
Possess enough enterprise customers, can also build up the more comprehensive personnel resume library of a ratio.
Enterprise customer can newly issue a position demand in cloud recruitment system or send out new use for having position
People applies, a position is needed to provide post title, and the length of service requires, educational requirement, region requirement, skill set requirements,
The descriptions such as responsibilities will also provide the registration mailbox or system identifier of one or more resume selection persons.For same position
It may consider multiple resume selection persons.
For resume selection person in the personnel resume for browsing system recommendation, which resume only simply skims through abstract, which
A little resumes are clicked and detailed examination, which resume are marked as that interview can be invited, and all can recruit system by cloud records automatically
Get off.Subsequent candidate people participated in interview after, resume selection person also need to fill in systems to candidate interview evaluation and
Marking.Here, the browsing situation of resume, detailed examination data knead dough examination invitation data, traditional internet recruitment website also may be used
To obtain, but specific resume selection people generally can not be accurately corresponded to, enterprise account can only be associated with.And candidate interviewing
It is then that conventional internet recruitment website can not obtain to assess information.
Further, in cloud recruitment system, it is by whether receiving offer after interview and entering trade-after for candidate
No to have passed through the trial period, resume selection person can also record in systems.
Some data instances of system record are as follows：
<uid：xiaoming@Atech.com,position_id:45678,cv_id:23653490012,action:
go_detail,time_stamp:1510578275>
<uid：limei@Atech.com,position_id:45678,cv_id:23653490012,action:
invitation,time_stamp:1510576491>
<uid：xiaoming@byteScience.com,position_id:31682, cv_id:23653490012,
action:invitation,time_stamp:1510590021>
Here uid has recorded enterprise's mailbox (one for screening person's information in above-described embodiment of resume selection person
Example), position_id has recorded the ID of specific position, and cv_id has recorded personnel resume ID, action and has recorded resume sieve
The action of the person of choosing including checking details (go_detail), invites the actions such as interview (invitation), time_stamp records
The timestamp that action occurs.
It, can be according to the side of any of the above-described embodiment after being collected into the resume feedback data of cloud recruitment platform user generation
Case trains corresponding resume assessment models according to these data.
The embodiment of the present invention additionally provides resume apparatus for evaluating, realizes to be such as computer installation including processor,
The computer installation further includes memory, wherein computer executable instructions can be stored with, can design corresponding meter as needed
Calculation machine executable instruction stores in memory so that processor is able to carry out this hair when running the corresponding computer instruction
Resume appraisal procedure in bright any embodiment.
Various embodiments of the present invention are described in detail above, but the present invention is not restricted to these specific embodiment,
Those skilled in the art can make a variety of variants and modifications embodiments on the basis of present inventive concept, these modifications and repair
Changing should all fall within scope of the present invention.
Claims (10)
1. a kind of resume appraisal procedure, including：
Obtain multiple first resumes and its first feedback data, in multiple first resumes with including positive feedback action item
The corresponding a part of first resume construction feature vector of one feedback data as multiple positive samples, in multiple first resumes with
Corresponding another part the first resume construction feature vector of the first feedback data including negative-feedback action item is as multiple negative samples
This, the positive feedback action item represents that the first resume is associated with positive feedback action, and the negative-feedback action item represents the first resume
It is associated with negative-feedback action；
Using the multiple positive sample and the multiple negative sample come Training valuation model learning described eigenvector with it is described just
Mapping relations between feedback action item；And
It is calculated by feature vector of the assessment models based on resume to be assessed according to the mapping relations described to be assessed
Assessment score of the resume about the positive feedback action item.
2. the method as described in claim 1 further includes：Corresponding weighting coefficient is set for different positive feedback actions,
And the positive sample is adjusted according to the weighting coefficient.
3. method as claimed in claim 2, wherein, the positive sample is adjusted according to the weighting coefficient and is included：
The sample weights of each positive sample are adjusted according to the weighting coefficient；Or
The sample copies of each positive sample is adjusted according to the weighting coefficient.
4. the method as described in claim 1 further includes：
The second feedback data of the resume to be assessed is obtained, whether positive feedback action item is included according to second feedback data
And the assessment score of the resume to be assessed, correct the mapping relations that the assessment models are learnt.
5. the method for claim 1, wherein the first resume construction feature vector is included：
Using first resume and the correlation that is associated between other resumes of any positive feedback action as feature
One of build the feature vector of the first resume.
6. the method for claim 1, wherein the positive feedback action includes resume selection person for first resume
Actions taken.
7. the method for claim 1, wherein first feedback data further includes auxiliary information, the assessment score
It is associated with the auxiliary information.
8. the method for claim 7, wherein, the auxiliary information include the first job information, the first trade information and/
Or feedback time information.
9. the method as described in any one of claim 1-8, further includes：
For the more parts of resumes to be assessed, the assessment score based on each resume to be assessed is to each letter to be assessed
It goes through and is ranked up.
10. a kind of resume apparatus for evaluating, including processor, which is characterized in that the processor runs scheduled computer instruction
To perform resume appraisal procedure as claimed in any one of claims 1-9 wherein.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120123956A1 (en) * | 2010-11-12 | 2012-05-17 | International Business Machines Corporation | Systems and methods for matching candidates with positions based on historical assignment data |
| CN105488662A (en) * | 2016-01-07 | 2016-04-13 | 北京歌利沃夫企业管理有限公司 | Bi-directional recommendation-based online recruitment system |
| CN107194762A (en) * | 2017-05-05 | 2017-09-22 | 腾讯科技（深圳）有限公司 | The vehicles recommend method, system and its equipment |
| CN107291715A (en) * | 2016-03-30 | 2017-10-24 | 阿里巴巴集团控股有限公司 | Resume appraisal procedure and device |
-
2017
- 2017-12-12 CN CN201711320131.8A patent/CN108153829B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120123956A1 (en) * | 2010-11-12 | 2012-05-17 | International Business Machines Corporation | Systems and methods for matching candidates with positions based on historical assignment data |
| CN105488662A (en) * | 2016-01-07 | 2016-04-13 | 北京歌利沃夫企业管理有限公司 | Bi-directional recommendation-based online recruitment system |
| CN107291715A (en) * | 2016-03-30 | 2017-10-24 | 阿里巴巴集团控股有限公司 | Resume appraisal procedure and device |
| CN107194762A (en) * | 2017-05-05 | 2017-09-22 | 腾讯科技（深圳）有限公司 | The vehicles recommend method, system and its equipment |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110909298A (en) * | 2019-12-03 | 2020-03-24 | 北京明略软件系统有限公司 | Score mapping creation method and device, computer equipment and readable storage medium |
| CN110909298B (en) * | 2019-12-03 | 2023-04-07 | 北京明略软件系统有限公司 | Score mapping creation method and device, computer equipment and readable storage medium |
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Address after: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Patentee after: Douyin Vision Co.,Ltd. Address before: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Patentee before: Tiktok vision (Beijing) Co.,Ltd. Address after: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Patentee after: Tiktok vision (Beijing) Co.,Ltd. Address before: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Patentee before: BEIJING BYTEDANCE NETWORK TECHNOLOGY Co.,Ltd. |
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