CN111198943B - Resume screening method and device and terminal equipment - Google Patents
Resume screening method and device and terminal equipment Download PDFInfo
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Abstract
The invention is applicable to the technical field of information processing, and provides a resume screening method, a resume screening device and terminal equipment, wherein the resume screening method comprises the following steps: collecting resume information of an recruiter to obtain a first resume; extracting keywords of the first resume to obtain a second resume with keywords; classifying the second resume according to preset recruitment positions and evaluating scores, wherein the number of the preset recruitment positions is two or more; and screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold. By the method, simultaneous recruitment of multiple positions can be achieved.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to a resume screening method, a resume screening device and terminal equipment.
Background
With the development of internet technology, more and more talent recruitment is realized through recruitment websites. Typically, the recruiter delivers the resume on a recruitment website, and the recruitment website performs a preliminary screening on the recruiter resume according to screening conditions set by the enterprise, thereby reducing the workload of the recruiter.
However, the resume screening system of the existing recruitment website can only set up to screen the resume of one position at a time, and is difficult to meet the requirement of simultaneous recruitment of multiple positions of an enterprise.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a resume screening method, a resume screening device and terminal equipment, which are used for solving the problem that simultaneous recruitment of multiple positions cannot be realized during network resume screening in the prior art.
A first aspect of an embodiment of the present invention provides a resume screening method, including:
collecting resume information of an recruiter to obtain a first resume;
Extracting keywords of the first resume to obtain a second resume with keywords;
Classifying the second resume according to preset recruitment positions and evaluating scores, wherein the number of the preset recruitment positions is two or more;
and screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold.
A second aspect of an embodiment of the present invention provides a resume screening apparatus, including:
the data acquisition unit is used for acquiring resume information of the recruiter to obtain a first resume;
The keyword extraction unit is used for extracting keywords from the first resume to obtain a second resume with keywords;
the resume classification evaluation unit is used for classifying the second resume according to preset recruitment positions and evaluating scores, wherein the number of the preset recruitment positions is two or more;
the resume screening unit is used for screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements steps of the resume screening method when the processor executes the computer program.
A fourth aspect of the embodiments of the present invention provides a computer readable storage medium storing a computer program which when executed by a processor implements steps of a resume screening method as described.
Compared with the prior art, the embodiment of the invention has the beneficial effects that: because the resume is classified and the scores are evaluated according to the preset recruitment positions after the keywords are extracted, the resume with all the scores higher than the preset screening threshold value in the recruitment positions can be screened out from the resume database at one time, the simultaneous recruitment of multiple positions is realized, and the talent recruitment efficiency of enterprises is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a first resume screening method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a second resume screening method according to an embodiment of the present application;
Fig. 3 is a schematic structural diagram of a resume screening device according to an embodiment of the present application;
Fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to illustrate the technical scheme of the application, the following description is made by specific examples.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application, the terms "first," "second," "third," etc. are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Embodiment one:
fig. 1 shows a flow chart of a first resume screening method according to an embodiment of the present application, which is described in detail below:
in S101, resume information of the recruiter is collected to obtain a first resume.
The resume information of the recruiter can be collected according to resume information filled in by the recruiter on a website or electronic resume directly delivered by the recruiter on the internet, so that a first resume is obtained and stored in a resume database.
The first resume may include at least one of a name, a gender, an academic calendar, a graduation institution, a specialty, a score, a community experience, and a social work experience of the recruiter.
In this embodiment, resume information of the recruiter is collected according to a collection range set by the user, where the collection range may be a website range corresponding to different websites, or may be a range corresponding to different pages under the same website.
In S102, extracting the keyword of the first resume to obtain a second resume with the keyword.
In order to quickly and accurately screen out the needed resume, each first resume in the resume database is subjected to keyword extraction to obtain a second resume with keywords so as to facilitate the subsequent resume screening. The number of keywords is a plurality, and words describing information such as academic institutions, graduation institutions, disciplines, english grades and the like can be included. For example, keywords describing a school may be primary, junior middle school, high school, family, research, doctor, etc.
In S103, the second resume is classified according to the preset recruitment positions and the score is evaluated, wherein the number of the preset recruitment positions is two or more.
Classifying the second resume with the keywords according to the preset recruitment positions, obtaining the classification probability that the second resume is classified to each preset recruitment position, and finally classifying the second resume to the preset recruitment position with the highest corresponding classification probability. And when the classification probabilities of the second resume being divided into the preset recruitment positions are the same and are the highest, the second resume is simultaneously distributed into the preset recruitment positions, namely one second resume corresponds to a plurality of recruitment positions. And after classification, respectively evaluating and scoring the second resume in the preset recruitment positions allocated with the second resume to obtain a second resume score S in the preset recruitment positions. The number of the preset recruitment positions is two or more, and the resume can be classified.
In S104, the corresponding first resume is screened according to the evaluation score of the second resume and a preset screening threshold.
And screening the corresponding first resume from the resume database according to the evaluation score S of the second resume in the divided preset recruitment position and the preset screening threshold F corresponding to the recruitment position. And if S is greater than or equal to F, extracting the first resume corresponding to the second resume from the resume database, otherwise, not extracting the resume, thereby finally screening the first resume corresponding to each preset recruitment position from the resume database.
The first resume corresponding to the second resume refers to the first resume before the second resume extracts the keywords, that is, the second resume is obtained by extracting the keywords according to the corresponding first resume. Specifically, a resume number ID is set in a first resume, a second resume is obtained by extracting a first resume keyword, the resume number ID of the first resume is copied to the second resume, and therefore the first resume and the second resume with the same resume number ID are in one-to-one correspondence with each other, and then the first resume with the same resume number ID can be extracted according to the resume number ID of the second resume.
In the embodiment of the invention, the resume is classified and evaluated according to the preset recruitment positions after the keywords are extracted, so that the resume with all the scores higher than the preset screening threshold value in the recruitment positions can be screened out from the resume database at one time, the simultaneous recruitment of multiple positions is realized, and the talent recruitment efficiency of enterprises is further improved.
Embodiment two:
fig. 2 shows a flow chart of a second resume screening method according to an embodiment of the present application, which is described in detail below:
In S201, resume information of the recruiter is collected to obtain a first resume.
In this embodiment, S201 is the same as S101 in the previous embodiment, and specific reference is made to the description of S101 in the first embodiment, which is not repeated here.
In S202, the keyword of the first resume is extracted to obtain a second resume having the keyword.
In order to quickly and accurately screen out the needed resume, each first resume in the resume database is subjected to keyword extraction to obtain a second resume with keywords so as to facilitate the subsequent resume screening.
Optionally, extracting the keywords of the first resume through a pre-trained keyword extraction model to obtain a second resume with keywords.
Specifically, a keyword extraction model is trained in advance through a deep learning algorithm, and the keyword extraction model is used for extracting keywords of a first resume, and because the deep learning algorithm can acquire more accurate information, when the keyword extraction model obtained through training of the deep learning algorithm is used for extracting the keywords of the first resume, effective information in the first resume can be extracted more accurately and efficiently, and a second resume only having keyword information concerned by enterprises is obtained.
Optionally, the pre-trained keyword extraction model specifically uses a keyword library as a tag, and uses a preset resume as a sample to train a model for extracting keywords.
In the model training stage, a keyword library is firstly established, and can comprise a academic keyword library, a college school name keyword library, a subject professional name keyword library, a college English level keyword library, a score keyword library, a practice company name and position keyword library, a working time and working position keyword library, a college time serving as a student working position keyword library, a job application position keyword library, a salary keyword library and the like, wherein a plurality of keyword libraries are selected according to specific requirements and packaged into a final keyword library. The plurality of keyword libraries selected here may be some or all combinations of the keyword libraries listed above, and may also include other keyword libraries not listed and set as needed.
After the keyword library is established, a model which can be used for extracting keywords is trained by taking the finally determined keyword library as a label and taking some resume information of the recruiters acquired from the Internet as a sample. The model extracts keywords in a sequence-to-sequence seq2seq (Sequence to Sequence) manner. Specifically, the training process of the model includes:
A1. Mapping each word in the first resume sentence into a word vector to obtain a matrix corresponding to the sentence.
Each word in the first resume sentence is mapped into an N-dimensional vector, the N-dimensional vector forms a word vector, the word vector is obtained by word vector algorithm word2vec (word to vector), and finally each sentence correspondingly obtains a Embedding matrix of T x N, wherein T is the length of the sentence.
A2. And encoding the Embedding matrix to obtain an encoded vector.
Each Embedding matrix is encoded into a corresponding 128-dimensional encoded vector using a single-layer Long-Term Memory network LSTM (Long Short-Term Memory) as the encoder Encoder and a 128-unit number as the single-layer LSTM number.
A3. and decoding the coded vector to obtain a decoding result.
The above-described encoded vector is input to the decoder at a time, the decoder uses the same model structure as the encoder, and then the decoding result is output from the decoder.
A4. And comparing the decoding result with the keyword library label, and optimizing a keyword extraction model.
And comparing the decoding result with the labels of the keyword library, and further correcting the decoding result, thereby improving the probability of outputting correct keywords each time so as to optimize a keyword extraction model.
In S203, receiving a setting instruction, where the setting instruction includes setting information of a recruitment position and setting information of a screening threshold; and carrying out resume classification screening according to the setting information.
Specifically, when the preset recruitment positions and the screening threshold values do not exist, or when preset information needs to be changed, a setting instruction is received, the recruitment positions and the corresponding screening threshold values are set according to the current recruitment needs, wherein the number of the set recruitment positions is multiple, and each recruitment position is correspondingly provided with one screening threshold value. And after setting, saving the setting information as a preset recruitment position and a corresponding preset screening threshold value for the follow-up resume classified screening.
In S204, the second resume is categorized according to the preset recruitment positions and the score is evaluated, wherein the number of preset recruitment positions is two or more.
Classifying the second resume with the keywords according to preset recruitment positions, wherein the number of the preset recruitment positions is two or more, and evaluating the score of the second resume divided into the recruitment positions. The number of the preset recruitment positions is two or more, and the resume can be classified.
Optionally, in step S204, the keyword of the first resume is extracted through a pre-trained keyword extraction model, so as to obtain a second resume with the keyword.
Specifically, the method comprises the following two steps:
S204.1, classifying the second resume according to the preset recruitment position through a pre-trained text classification evaluation model, and returning the classification probability of classifying the second resume to the preset recruitment position.
Specifically, the step S204.1 includes:
S204.1B1, a second resume only with keyword information is also used for obtaining a corresponding word vector Embedding matrix through a word2vec algorithm, wherein the dimension of the matrix is T.N, T is the sentence length, and N is the dimension of each word.
S204.1B2, carrying out convolution operation on Embbeding matrixes by using a convolution layer containing M convolution kernels with the size of H x N to obtain corresponding feature map. Wherein M is the number of convolution kernel channels, preferably 9, one channel represents one convolution kernel, and one convolution kernel corresponds to one feature map; h is the length of one side of the convolution kernel, preferably 3, and the length of the other side of the convolution kernel is equal to the length N of the sentence.
S204.1B3, obtaining the maximum value max_value in each feature map through the max-pool of the maximum pooling layer, and performing cascading operation on all max_values to obtain a matrix of 1*M.
S204.1B4, according to the matrix of 1*M and the preset recruitment positions, the classification probability of the corresponding second resume to each preset recruitment position is obtained by judging the matrix content through a softmax multi-classifier, and the classification probability of the second resume to which preset recruitment position is higher is judged, so that the second resume is finally classified into the preset recruitment position with the highest corresponding classification probability, and the classification probability of the second resume to the preset recruitment position is returned. And when the classification probabilities of the second resume being divided into the preset recruitment positions are the same and are the highest, the second resume is simultaneously distributed into the preset recruitment positions, namely one second resume corresponds to a plurality of recruitment positions.
S204.2, determining the evaluation score of the second resume according to the classification probability.
And determining an evaluation score S of the second resume according to the classification probability P returned when the second resume is classified to the corresponding preset recruitment position, wherein specifically, S=P×100.
In S205, the corresponding first resume is screened according to the evaluation score of the second resume and a preset screening threshold.
In this embodiment, S205 is the same as S104 in the previous embodiment, and the detailed description of S104 in the first embodiment is omitted here.
Optionally, after step S205, further includes: and sending the screened first resume.
And sending the resume screened from the resume database to the enterprise, and sending the screened resume to a recruitment department through pre-stored mailbox information of the recruitment department so as to inform recruiters of the enterprise in time. Specifically, a time period can be preset, and the first resume corresponding to each preset recruitment position is respectively packaged and sent to the enterprise at intervals, so that the enterprise can conveniently conduct subsequent recruitment work.
According to the embodiment of the invention, the recruitment positions and the screening threshold are set according to the recruitment requirement, and the multiple recruitment positions are set, so that the resume is classified and the scores are evaluated according to the multiple recruitment positions after keyword extraction is performed on the resume, and therefore, the resume with all the scores higher than the preset screening threshold in the multiple recruitment positions can be screened from the resume database at one time, simultaneous recruitment of multiple positions is realized, and talent recruitment efficiency of enterprises is further improved.
Embodiment III:
fig. 3 is a schematic structural diagram of a resume screening device according to an embodiment of the present application, and for convenience of explanation, only parts related to the embodiment of the present application are shown:
the resume screening device comprises: the system comprises a data acquisition unit 31, a keyword extraction unit 32, a resume classification evaluation unit 33 and a resume screening unit 34. Wherein:
the data acquisition unit 31 is configured to acquire resume information of the recruiter to obtain a first resume.
The resume information of the recruiter can be collected according to resume information filled in by the recruiter on a website or electronic resume directly delivered by the recruiter on the internet, so that a first resume is obtained and stored in a resume database.
The first resume may include at least one of a name, a gender, an academic calendar, a graduation institution, a specialty, a score, a community experience, and a social work experience of the recruiter.
In this embodiment, resume information of the recruiter is collected according to a collection range set by the user, where the collection range may be a website range corresponding to different websites, or may be a range corresponding to different pages under the same website.
And the keyword extraction unit 32 is configured to perform keyword extraction on the first resume to obtain a second resume with a keyword.
In order to quickly and accurately screen out the needed resume, each first resume in the resume database is subjected to keyword extraction to obtain a second resume with keywords so as to facilitate the subsequent resume screening. The number of keywords is a plurality, and words describing information such as academic institutions, graduation institutions, disciplines, english grades and the like can be included. For example, keywords describing a school may be primary, junior middle school, high school, family, research, doctor, etc.
Optionally, the keyword extraction unit extracts the keywords of the first resume through a pre-trained keyword extraction model to obtain a second resume with keywords.
Specifically, a keyword extraction model is trained in advance through a deep learning algorithm, and the keyword extraction model is used for extracting keywords of a first resume, and because the deep learning algorithm can acquire more accurate information, when the keyword extraction model obtained through training of the deep learning algorithm is used for extracting the keywords of the first resume, effective information in the first resume can be extracted more accurately and efficiently, and a second resume only having keyword information concerned by enterprises is obtained.
Optionally, the keyword extraction unit includes a keyword extraction model training module, configured to train a model for extracting keywords by using a keyword library as a tag and a preset resume as a sample.
In the model training stage, a keyword library is firstly established, and can comprise a academic keyword library, a college school name keyword library, a subject professional name keyword library, a college English level keyword library, a score keyword library, a practice company name and position keyword library, a working time and working position keyword library, a college time serving as a student working position keyword library, a job application position keyword library, a salary keyword library and the like, wherein a plurality of keyword libraries are selected according to specific requirements and packaged into a final keyword library. The plurality of keyword libraries selected here may be some or all combinations of the keyword libraries listed above, and may also include other keyword libraries not listed and set as needed.
After the keyword library is established, a model which can be used for extracting keywords is trained by taking the finally determined keyword library as a label and taking some resume information of the recruiters acquired from the Internet as a sample.
The resume classification evaluation unit 33 is configured to classify the second resume according to preset recruitment positions and evaluate scores, where the number of preset recruitment positions is two or more.
Classifying the second resume with the keywords according to the preset recruitment positions, obtaining the classification probability that the second resume is classified to each preset recruitment position, and finally classifying the second resume to the preset recruitment position with the highest corresponding classification probability. And when the classification probabilities of the second resume being divided into the preset recruitment positions are the same and are the highest, the second resume is simultaneously distributed into the preset recruitment positions, namely one second resume corresponds to a plurality of recruitment positions. And after classification, respectively evaluating and scoring the second resume in the preset recruitment positions allocated with the second resume to obtain a second resume score S in the preset recruitment positions. The number of the preset recruitment positions is two or more, and the resume can be classified.
Optionally, the resume evaluation unit classifies the second resume according to a preset recruitment position and evaluates the score through a pre-trained text classification evaluation model.
Optionally, the resume classification evaluation unit comprises a classification module and an evaluation score module.
The classification module is used for classifying the second resume according to the preset recruitment position through a pre-trained text classification evaluation model, and returning the classification probability of dividing the second resume to the preset recruitment position.
And the evaluation score module is used for determining the evaluation score of the second resume according to the classification probability.
The resume screening unit 34 is configured to screen the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold.
Optionally, the resume screening device further includes a setting unit, configured to receive a setting instruction, where the setting instruction includes setting information of a recruitment position and setting information of a screening threshold; and carrying out resume classification screening according to the setting information.
Specifically, when the preset recruitment positions and the screening threshold values do not exist, or when preset information needs to be changed, a setting instruction is received, the recruitment positions and the corresponding screening threshold values are set according to the current recruitment needs, wherein the number of the set recruitment positions is multiple, and each recruitment position is correspondingly provided with one screening threshold value. And after setting, saving the setting information as a preset recruitment position and a corresponding preset screening threshold value for the follow-up resume classified screening.
Optionally, the resume screening device further includes a sending unit, configured to send the screened first resume.
And sending the resume screened from the resume database to the enterprise, and sending the screened resume to a recruitment department through pre-stored mailbox information of the recruitment department so as to inform recruiters of the enterprise in time. Specifically, a time period can be preset, and the first resume corresponding to each preset recruitment position is respectively packaged and sent to the enterprise at intervals, so that the enterprise can conveniently conduct subsequent recruitment work.
In the embodiment of the invention, the number of the preset recruitment positions is multiple, and the resume is classified and the scores are evaluated according to the preset recruitment positions after the keyword extraction, so that the resume with all the scores higher than the preset screening threshold value in the multiple recruitment positions can be screened out from the resume database at one time, the simultaneous recruitment of multiple positions is realized, and the talent recruitment efficiency of enterprises is further improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Embodiment four:
Fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in fig. 4, the terminal device 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in the memory 41 and executable on the processor 40. The steps of the respective resume screening method embodiments described above, such as steps S101 to S104 shown in fig. 1, are implemented when the processor 40 executes the computer program 42. Or the processor 40, when executing the computer program 42, performs the functions of the modules/units of the device embodiments described above, such as the functions of the units 31 to 34 shown in fig. 3.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 42 in the terminal device 4. For example, the computer program 42 may be divided into a data acquisition unit, a keyword extraction unit, a resume classification evaluation unit, a resume screening unit, each unit having the following specific functions:
The data acquisition unit is used for acquiring resume information of the recruiter to obtain a first resume.
And the keyword extraction unit is used for extracting keywords from the first resume to obtain a second resume with keywords.
The resume classification evaluation unit is used for classifying the second resume according to preset recruitment positions and evaluating scores, wherein the number of the preset recruitment positions is two or more.
The resume screening unit is used for screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold.
The terminal device 4 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the terminal device 4 and does not constitute a limitation of the terminal device 4, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The Processor 40 may be a central processing unit (Central Processing Unit, CPU), other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing the computer program as well as other programs and data required by the terminal device. The memory 41 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (8)
1. The resume screening method is characterized by comprising the following steps of:
collecting resume information of an recruiter to obtain a first resume;
Extracting keywords of the first resume to obtain a second resume with keywords;
Classifying the second resume according to preset recruitment positions and evaluating scores, wherein the number of the preset recruitment positions is two or more;
screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold;
the classifying the second resume according to the preset recruitment position and evaluating the score specifically includes:
B1. Vectorization processing is carried out on the second resume only with the keyword information, and a corresponding word vector Embedding matrix is obtained;
B2. Performing convolution operation on the Embbeding matrix by using a convolution layer containing M convolution kernels with the size of H x N to obtain a corresponding feature map; wherein M is the number of convolution kernel channels, one channel represents one convolution kernel, and one convolution kernel corresponds to one feature map; h is the length of one side of the convolution kernel, and the length of the other side of the convolution kernel is equal to the length N of the sentence;
B3. obtaining a maximum value max_value in each feature map through a max-pool of a maximum pooling layer, and performing cascading operation on all max_values to obtain a matrix of 1*M;
B4. According to the matrix of 1*M and the preset recruitment positions, the classification probability of the corresponding second resume to each preset recruitment position is obtained by judging the matrix content by using a softmax multi-classifier, and the classification probability of the second resume to which preset recruitment position is higher is judged, so that the second resume is finally classified into the preset recruitment position with the highest corresponding classification probability, and the classification probability of the second resume to the preset recruitment position is returned;
And determining an evaluation score of the second resume according to the classification probability.
2. The resume screening method of claim 1, wherein the extracting the keywords of the first resume to obtain a second resume with keywords specifically comprises:
And extracting the keywords of the first resume through a pre-trained keyword extraction model to obtain a second resume with keywords.
3. The resume screening method of claim 2, wherein the pre-trained keyword extraction model is specifically:
And training a model for extracting the keywords by taking the keyword library as a label and taking a preset resume as a sample.
4. The resume screening method of claim 1, further comprising, prior to said classifying the second resume according to a preset recruitment position and evaluating a score:
receiving a setting instruction, wherein the setting instruction comprises setting information of a recruitment position and setting information of a screening threshold value;
and carrying out resume classification screening according to the setting information.
5. The resume screening method according to any one of claims 1 to 4, further comprising, after the screening of the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold:
and sending the screened first resume.
6. A resume screening device, comprising:
the data acquisition unit is used for acquiring resume information of the recruiter to obtain a first resume;
The keyword extraction unit is used for extracting keywords from the first resume to obtain a second resume with keywords;
the resume classification evaluation unit is used for classifying the second resume according to preset recruitment positions and evaluating scores, wherein the number of the preset recruitment positions is two or more;
the resume screening unit is used for screening the corresponding first resume according to the evaluation score of the second resume and a preset screening threshold value;
An evaluation scoring module for:
B1. Vectorization processing is carried out on the second resume only with the keyword information, and a corresponding word vector Embedding matrix is obtained;
B2. Performing convolution operation on the Embbeding matrix by using a convolution layer containing M convolution kernels with the size of H x N to obtain a corresponding feature map; wherein M is the number of convolution kernel channels, one channel represents one convolution kernel, and one convolution kernel corresponds to one feature map; h is the length of one side of the convolution kernel, and the length of the other side of the convolution kernel is equal to the length N of the sentence;
B3. obtaining a maximum value max_value in each feature map through a max-pool of a maximum pooling layer, and performing cascading operation on all max_values to obtain a matrix of 1*M;
B4. according to the matrix of 1*M and the preset recruitment positions, the classification probability of the corresponding second resume to each preset recruitment position is obtained by judging the matrix content by using a softmax multi-classifier, and the classification probability of the second resume to which preset recruitment position is higher is judged, so that the second resume is finally classified into the preset recruitment position with the highest corresponding classification probability, and the classification probability of the second resume to the preset recruitment position is returned; and determining an evaluation score of the second resume according to the classification probability.
7. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 5.
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| CN113610503B (en) * | 2021-08-11 | 2024-06-21 | 中国平安人寿保险股份有限公司 | Resume information processing method, device, equipment and medium |
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