CN117196550A - Talent and enterprise supply and demand matching method and system - Google Patents
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Abstract
The invention relates to the technical field of information processing, in particular to a method and a system for matching talents with enterprise supply and demand, wherein the method comprises the following steps: acquiring the demand information of an enterprise target post, and preprocessing the demand information; extracting capacity factors required by a target post according to the requirement information; setting the weight corresponding to the capacity factor; combining the capacity factors and the corresponding weights to construct a human enterprise matching model; obtaining an evaluation result of the capacity factor by the candidate personnel; and combining the human-enterprise matching model and the evaluation result to obtain the matching degree of the candidate personnel and the target post. According to the invention, through analyzing the capacity quality required by the enterprise posts, a talent and enterprise matching model is established, and the matching result is fed back to the user, so that the problems of asymmetric information, low matching efficiency and the like in the talent and enterprise supply and demand matching process are solved, and the accurate matching of talent and enterprise supply and demand is realized.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a method and a system for matching talents with enterprise supply and demand.
Background
With the development of economy and the advancement of society, the problem of matching supply and demand between talents and enterprises is receiving more and more attention. The conventional talent and enterprise supply and demand matching method mainly depends on manual experience and a simple information processing technology, so that problems of information asymmetry, low matching efficiency and the like often occur by adopting the conventional matching method, accurate matching of talents and enterprises is difficult to realize, effective recruitment strategies cannot be provided for the enterprises, effective suggestions are provided for talent development planning, and personalized requirements of the enterprises and the talents are difficult to meet. Therefore, a scientific, efficient and accurate talent and enterprise supply and demand matching method is needed to solve the problem of matching talents and enterprise supplies and demands.
Disclosure of Invention
Aiming at the defects of the existing method and the requirements of practical application, in order to solve the problem of accurate matching of talents and enterprise supply and demand, the invention provides a method for matching the talents and the enterprise supply and demand. The method for matching talents with enterprise supply and demand comprises the following steps: acquiring the demand information of an enterprise target post, and preprocessing the demand information; extracting capacity factors required by a target post according to the requirement information; setting the weight corresponding to the capacity factor; combining the capacity factors and the corresponding weights to construct a human enterprise matching model; obtaining an evaluation result of the capacity factor by the candidate personnel, wherein the number of the candidate personnel comprises one or more than one; and combining the human-enterprise matching model and the evaluation result to obtain the matching degree of the candidate personnel and the target post. According to the invention, through analyzing the capacity quality required by the enterprise posts, a talent and enterprise matching model is established, and the matching result is fed back to the user, so that the problems of asymmetric information, low matching efficiency and the like in the talent and enterprise supply and demand matching process are solved, and the accurate matching of talent and enterprise supply and demand is realized.
Optionally, constructing a capability factor evaluation index system of the target post according to the requirement information, wherein the capability factor evaluation index system comprises two or more capability factors; based on the capability factor evaluation index system, evaluating the importance of each capability factor; constructing a capacity factor weight calculation matrix according to the evaluation result; and setting the weight of the capacity factor through the capacity factor weight calculation matrix. The weight of the capacity factor is determined and set through a systematic method, so that the characteristics and the importance of the target post requirement are captured more accurately, and the accuracy and the efficiency of the matching model are improved.
Optionally, the constructing a capability factor evaluation index system of the target post according to the requirement information includes the following steps: aiming at the requirement information of a target post, dividing all capacity factors into primary capacity factors and secondary capacity factors; and constructing a capability factor evaluation index system of the target post through the primary capability factors and the secondary capability factors, wherein the capability factor evaluation index system comprises a plurality of primary capability factors, and any one of the primary capability factors comprises one or more secondary capability factors. According to the method, a finer evaluation index system is constructed by subdividing capacity factors into a first level and a second level, so that the matching model can more accurately reflect the requirements of a target post. The systematic and hierarchical evaluation method is beneficial to improving the accuracy and efficiency of the matching model, so that the demand of matching talents with the supply and demand of enterprises is better met.
Optionally, the evaluating the importance of each capability factor based on the capability factor evaluation index system includes the following steps: extracting each level of capability factors in the capability factor evaluation index system to respectively obtain capability factor vectors of corresponding levels; for the capability factor vector of the kth level, the importance (a) of one capability factor relative to the other capability factor in the capability factor vector is respectively evaluated according to an evaluation rule i ,a j ) The evaluation rule is as follows: on the target post, when the capacity factor a i And capability factor a j At the same critical scale, then (a i ,a j ) =0; when the ability factor a i Specific energy factor a j One important dimension higher, then (a i ,a j ) =0.01; when the ability factor a i Specific energy factor a j Two important dimensions higherThen (a) i ,a j ) =0.02; when the ability factor a i Specific energy factor a j Three important dimensions higher, then (a) i ,a j ) =0.03; when the ability factor a i Specific energy factor a j Four important dimensions higher, then (a) i ,a j ) =0.04; when the ability factor a i Specific energy factor a j Five important dimensions higher, then (a) i ,a j ) =0.05; when the ability factor a i Specific energy factor a j One important dimension lower, then (a) i ,a j ) -0.01; when the ability factor a i Specific energy factor a j Two important dimensions lower, then (a) i ,a j ) -0.02; when the ability factor a i Specific energy factor a j Three important dimensions lower, then (a) i ,a j ) -0.03; when the ability factor a i Specific energy factor a j Four important dimensions lower, then (a) i ,a j ) -0.04; when the ability factor a i Specific energy factor a j Five important dimensions lower, then (a) i ,a j ) -0.05. The present option numerically represents their relative importance by placing different capability factors on different important scales. The accurate importance assessment method is beneficial to constructing an accurate capacity factor weight calculation matrix, so that the requirement of a target post is better reflected in the matching model, and the accuracy and the efficiency of the matching model are improved.
Optionally, the constructing the capacity factor weight calculation matrix according to the evaluation result includes the following steps: aiming at any level of capacity factors, constructing a capacity factor weight calculation matrix by combining the importance of the capacity factors with the corresponding level capacity factor vector, wherein the capacity factor weight calculation matrix G corresponding to the kth level capacity factor vector k The following model is satisfied:wherein (a) 1 ,a 1 )＝0，(a 1 ,a 1 ) Representing the importance of the 1 st capability factor in the k-th capability factor vector compared to the 1 st capability factor, (a) 1 ,a m ) Representing the importance of the 1 st capability factor in the k-th capability factor vector compared to the m-th capability factor, (a) m ,a 1 ) Representing the importance of the mth capability factor in the kth capability factor vector compared to the 1 st capability factor, (a) m ,a m )＝0，(a m ,a m ) The importance of the mth capability factor in the kth capability factor vector compared to the mth capability factor is represented. The present alternative provides an explicit method for constructing the capacity factor weight calculation matrix that accurately expresses the relative importance between different capacity factors using the evaluation rules and capacity factor vectors. The method is helpful for ensuring that the weight used by the matching model can reflect the requirement of the target post more accurately, and improves the accuracy and efficiency of supply and demand matching.
Optionally, the obtaining the weight of any one capacity factor relative to all capacity factors through the capacity factor weight calculation matrix includes the following steps: according to the capacity factor weight calculation matrix and the corresponding evaluation model, evaluating the importance degree of any capacity factor, wherein the evaluation model corresponding to the kth-level capacity factor vectorThe following formula is satisfied: />Wherein (1)>Representing the weight of the ith capability factor in the kth level capability factor vector, m representing the number of all capability factors in the kth level capability factor vector. The assessment model provided by this alternative determines the weights by averaging the relevance of each capacity factor to the other capacity factors. The assessment model is helpful for accurately measuring the importance of each capacity factor in the matching model, so that the accuracy and the effectiveness of matching talents with enterprise supply and demand are improved.
Optionally, the human enterprise matching model satisfies the following formula： Wherein R is ε Indicating the matching degree of the candidate person and the target post, wherein N indicates the number of primary capacity factors,/or%>The weight of the ith primary capability factor is represented, M represents the number of all secondary capability factors corresponding to the ith primary capability factor, +.>A weight representing a j-th secondary capability factor corresponding to the i-th primary capability factor, c ij And (5) representing the evaluation result of the j-th secondary capacity factor corresponding to the i-th primary capacity factor of the candidate person. The human enterprise matching model provided by the selectable item utilizes the weight of the primary and secondary capacity factors and the evaluation result of the candidate personnel to calculate the matching degree. The human enterprise matching model provided by the selectable item can more accurately reflect the matching degree between the candidate personnel and the posts, and is based on multi-level capacity factors and weight information. This helps to more accurately determine the most appropriate candidates for the post, improving the quality of supply and demand matching.
Optionally, the method for matching talents with enterprise supply and demand further comprises the following steps: sorting the matching degree of a plurality of candidate persons according to the size; setting a distinguishing threshold, and extracting candidate personnel evaluation results corresponding to the distinguishing threshold from the sorting results according to the distinguishing threshold, wherein the matching degree difference does not meet the distinguishing threshold; and based on the capability factor evaluation index system, removing the evaluation result with the minimum first-level capability factor weight, updating the matching degree of the corresponding candidate personnel, and re-sequencing the updated matching degree until the matching degree difference of any two candidate personnel in the sequencing result meets the distinguishing threshold value. The method comprises the steps of sorting the matching degree of candidate persons, setting a distinguishing threshold value, and then eliminating the evaluation results of the candidate persons with the matching degree difference larger than the threshold value. And then, eliminating the primary capability factor with the minimum weight according to the capability factor evaluation index system, updating the matching degree of the corresponding candidate personnel, and continuously iterating until the matching degree gap meets a threshold value. This helps to more finely screen the most appropriate candidate, improving matching efficiency and accuracy.
Optionally, the updated matching degree satisfies the following model: wherein R is ′ ε Indicating the matching degree of the candidate person and the target post, N ′ Representing the number of remaining primary capacity factors, N ′ < N, N represents the number of primary capacity factors in the original capacity factor evaluation index system, < ->The weight of the ith primary capability factor is represented, M represents the number of all secondary capability factors corresponding to the ith primary capability factor, +.>A weight representing a j-th secondary capability factor corresponding to the i-th primary capability factor, c ij And (5) representing the evaluation result of the j-th secondary capacity factor corresponding to the i-th primary capacity factor of the candidate person.
In a second aspect, to be able to efficiently perform a method for matching talents with supply and demand of an enterprise provided by the present invention, the present invention also provides a system for matching talents with supply and demand of an enterprise, where the system includes a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is configured to store a computer program, where the computer program includes program instructions, and where the processor is configured to invoke the program instructions to perform the method for matching talents with supply and demand of an enterprise according to the first aspect of the present invention. The system for matching talents with enterprise supply and demand has compact structure and stable performance, and can stably execute the method for matching talents with enterprise supply and demand, thereby improving the overall applicability and practical application capability of the system.
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FIG. 1 is a flow chart of a method for matching talents with enterprise supply and demand according to an embodiment of the present invention;
FIG. 2 is a block diagram of a system for matching talents with enterprise supply and demand according to an embodiment of the present invention.
Detailed Description
Specific embodiments of the invention will be described in detail below, it being noted that the embodiments described herein are for illustration only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: no such specific details are necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been described in detail in order not to obscure the invention.
Throughout the specification, references to "one embodiment," "an embodiment," "one example," or "an example" mean: a particular feature, structure, or characteristic described in connection with the embodiment or example is included within at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "one example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and that the illustrations are not necessarily drawn to scale.
In an alternative embodiment, referring to fig. 1, fig. 1 is a flowchart of a method for matching talents with enterprise supply and demand according to an embodiment of the present invention. As shown in fig. 1, the method for matching talents with enterprise supply and demand includes the following steps:
s01, acquiring requirement information of a target post of an enterprise, and preprocessing the requirement information.
In this embodiment, the step S01 of obtaining the requirement information of the target post of the enterprise may obtain the requirement information of the post of the enterprise through the online recruitment platform such as intelligent recruitment, forward careless, BOSS direct recruitment, offline recruitment, official website of the enterprise, social media platform and other channels and network searching, and integrate the obtained information of the post of the enterprise.
In this embodiment, the post information includes, but is not limited to, post name, post responsibilities, job conditions, work and personality requirements, work conditions and physical environment, social environment, and the like. Further, preprocessing the acquired business post information includes, but is not limited to, the following operations:
1. the text of the post information is cleaned to remove unnecessary special characters, punctuation marks, HTML tags and other noise data for subsequent text processing and analysis.
2. The post description text is segmented, long sentences are divided into word sequences, and corresponding parts of speech are marked for each word, so that further semantic analysis and processing are facilitated.
3. Removing common stop words; keywords in the post information are extracted to identify key responsibilities, skill requirements, and other important information.
4. Text data is converted into a numerical vector representation for machine learning and training and application of matching models.
5. And carrying out normalization processing on the numerical value type characteristics, and unifying the data of different scales in the same range so as to improve the convergence speed and accuracy of the matching model.
Noise and redundant information can be reduced through preprocessing the post information, useful characteristics are extracted, and further establishment of a matching model of subsequent talents and enterprises is supported. Specifically, the pretreatment method and the pretreatment steps of the post information can be adjusted and combined according to specific situations and requirements.
S02, extracting capacity factors required by the target post according to the requirement information.
In an alternative embodiment, the capacity factor required for extracting the target post according to the requirement information in step S02 includes the following steps:
s021, carrying out keyword frequency statistics on the post information after pretreatment, identifying frequently-occurring keywords, and further calculating the correlation degree between different keywords in the post information.
Because the keywords with higher correlation degree are more representative and important, the keywords can be used as the basis of the extraction capacity factors. In this embodiment, the relevance between keywords is evaluated using a word frequency-inverse document frequency (TF-IDF) calculation method, i.e., the importance of a word in text is measured by the product of the word frequency of the word and the inverse document frequency.
Word frequency (TF) refers to the frequency with which a word appears in a document, and the word frequency is calculated to satisfy the following formula: TF (w, d) = (number of times word w appears in document d)/(total number of words in document d). The Inverse Document Frequency (IDF) is used to measure the importance of a word to the entire text set, and the inverse document frequency is calculated to satisfy the following formula: IDF (w) =lg (corpus document total/document number containing word w). The TF-IDF value of each word in the document satisfies the following formula: TF-IDF (w, d) =tf (w, d) ×idf (w).
It will be appreciated that the higher the TF-IDF value, the higher the importance of the word in the document.
S022, calculating the similarity between the keywords through cosine similarity, wherein the calculation of the cosine similarity satisfies the following formula: similarity=a·b/(|a|b||), where a and B represent two keyword vectors, respectively. The value range of the similarity is between-1 and 1, the closer the value of the similarity is to 1, the more similar the keywords are, the closer the value of the similarity is to-1, the less similar the keywords are, and the value of the similarity is to 0, so that no obvious similarity exists between the keywords.
S023, screening out keywords with correlation higher than a threshold value by setting a similarity threshold value.
Further, the similarity threshold may be set according to actual situations and application requirements.
S024, constructing a capacity factor extraction model, wherein the capacity factor extraction model takes a TF-IDF value and keyword similarity as inputs and capacity factors as outputs.
And training the capacity factor extraction model through a related sample data set, evaluating the capacity factor extraction model after the training of the capacity factor extraction model is completed, ensuring the effectiveness of the capacity factor extraction model, and optimizing the capacity factor extraction model according to an evaluation result.
Further, the extracted capacity factors are output through the capacity factor extraction model after training is completed. Furthermore, for a particular industry post, the capacity factor may be extracted with reference to industry standards or requirements of related qualification.
S03, setting the weight corresponding to the capacity factor;
based on the above embodiment, the capability factor required by the target post is extracted by using the requirement information, and the setting of the weight corresponding to the capability factor in step S03 includes, in one or some other embodiments, the following steps:
s031, constructing a capability factor evaluation index system of a target post according to the requirement information, wherein the capability factor evaluation index system comprises two or more capability factors.
Further, in this embodiment, all the capacity factors are divided into a primary capacity factor and a secondary capacity factor according to the requirement information of a certain target post; and constructing a capability factor evaluation index system of the target post through the primary capability factor and the secondary capability factor, wherein the capability factor evaluation index system comprises a plurality of primary capability factors, and any one of the primary capability factors comprises one or a plurality of secondary capability factors.
Specifically, the primary capability factors for the "human resource manager" post include: knowledge, skills and competence, occupational literacy, experience, and personal characteristics; further, the secondary capability factors for the primary capability factor "knowledge" in this "human resource manager" post include: company knowledge, expertise, and management knowledge; the secondary capability factors of the primary capability factor "skill and capability" include: computer operability, problem analysis capability, presentation capability, management capability, and decision capability; the secondary competence factors of the primary competence factor "professional literacy" include: responsibility center, core taking and principle; the primary capacity factors "experience" secondary capacity factors include: educational background, practice experience, and work experience; the secondary competence factors of the primary competence factor "professional literacy" include: responsibility center, core taking and principle; the secondary capability factors of the primary capability factor "personal property" include: hobbies and personality preferences.
S032, evaluating the importance of each capacity factor based on the capacity factor evaluation index system.
Further, in this embodiment, the evaluating the importance of each capacity factor based on the capacity factor evaluation index system includes the following steps:
s0321, extracting all levels of capacity factors in the capacity factor evaluation index system to obtain capacity factor vectors of corresponding levels respectively.
Specifically, the kth-level capacity factor vector in the capacity factor evaluation index system satisfies the following model: i k ＝(a 1 ,…,a m ) k Where k=1, 2, …, n, k represents the level of the capacity factor, n represents the level of the capacity factor evaluation index system, I k Represents a k-th level capability factor vector, a 1 Representing the 1 st capability factor in the capability factor vector, a m Represents the mth capability factor in the capability factor vector, and m represents the number of capability factors in the k-th capability factor vector.
S0322, for the capacity factor vector of any level, respectively evaluating the importance (a) of one capacity factor relative to the other capacity factor in the capacity factor vector according to an evaluation rule i ,a j )。
Specifically, the evaluation rule is as follows: at the target postOn bit, when the ability factor a i And capability factor a j At the same critical scale, then (a i ,a j ) =0; when the ability factor a i Specific energy factor a j One important dimension higher, then (a i ,a j ) =0.01; when the ability factor a i Specific energy factor a j Two important dimensions higher, then (a) i ,a j ) =0.02; when the ability factor a i Specific energy factor a j Three important dimensions higher, then (a) i ,a j ) =0.03; when the ability factor a i Specific energy factor a j Four important dimensions higher, then (a) i ,a j ) =0.04; when the ability factor a i Specific energy factor a j Five important dimensions higher, then (a) i ,a j ) =0.05; when the ability factor a i Specific energy factor a j One important dimension lower, then (a) i ,a j ) -0.01; when the ability factor a i Specific energy factor a j Two important dimensions lower, then (a) i ,a j ) -0.02; when the ability factor a i Specific energy factor a j Three important dimensions lower, then (a) i ,a j ) -0.03; when the ability factor a i Specific energy factor a j Four important dimensions lower, then (a) i ,a j ) -0.04; when the ability factor a i Specific energy factor a j Five important dimensions lower, then (a) i ,a j )＝-0.05。。
It will be appreciated that the importance scale is a measure for measuring and comparing the relative importance between different capacity factors, which is set and evaluated to determine which capacity factors are more important for a particular target post in talent to enterprise supply and demand matching, thereby providing a weight for matching calculation.
Further, evaluation rules are used to quantify the relative importance between capacity factors in order to more accurately assign weights to these factors. For example, if in the capacity factor evaluation of sales positions, the communication capacity is one scale higher in importance than the sales experience, the corresponding important scale may be set to 0.01. This means that in the matching calculation, the system will take into account the communication ability with a slightly higher weight than the sales experience. The specific implementation of the evaluation of the critical dimension depends on the design and algorithm of the system. Typically, this can be done by specialized algorithms or according to the knowledge of the domain expert.
S0323, constructing a capacity factor weight calculation matrix according to the evaluation result.
In this embodiment, the constructing a capacity factor weight calculation matrix according to the evaluation result includes the following steps: and constructing a capacity factor weight calculation matrix according to the importance of the corresponding capacity factor vector and the capacity factor aiming at any level of capacity factor. The capacity factor weight calculation matrix is used for representing the relative weights among different capacity factors. It reflects the importance of each capability factor to the target post, and these weights are determined based on the evaluation results.
Further, a capacity factor weight calculation matrix G corresponding to the k-th capacity factor vector k The following model is satisfied:wherein (a) 1 ,a 1 )＝0，(a 1 ,a 1 ) Representing the importance of the 1 st capability factor in the k-th capability factor vector compared to the 1 st capability factor, (a) 1 ,a m ) Representing the importance of the 1 st capability factor in the k-th capability factor vector compared to the m-th capability factor, (a) m ,a 1 ) Representing the importance of the mth capability factor in the kth capability factor vector compared to the 1 st capability factor, (a) m ,a m )＝0，(a m ,a m ) The importance of the mth capability factor in the kth capability factor vector compared to the mth capability factor is represented.
S0324, setting the weight of the capacity factor through the capacity factor weight calculation matrix.
In this embodiment, the obtaining the weight of any one capacity factor relative to all capacity factors through the capacity factor weight calculation matrix includes the following steps: and according to the capacity factor weight calculation matrix and the corresponding evaluation model, evaluating the importance degree of any capacity factor. Further, the assessment model is used to determine final capacity factor weights from the importance values in the capacity factor weight calculation matrix.
Specifically, the evaluation model corresponding to the k-th level capacity factor vectorThe following formula is satisfied:wherein (1)>Representing the weight of the ith capability factor in the kth level capability factor vector, m representing the number of all capability factors in the kth level capability factor vector. The evaluation model provided by the implementation can allocate proper weights for different capacity factors so as to more accurately reflect the relative importance of the capacity factors in the matching degree calculation.
S04, combining the capacity factors and the corresponding weights to construct a human enterprise matching model.
It can be appreciated that the invention constructs a matching model of the human enterprise by combining the capacity factors and the corresponding weights for the candidate human and the target enterprise.
In this embodiment, based on the above-mentioned two-level capability factor evaluation index system, the corresponding human enterprise matching model satisfies the following formula:wherein R is ε Indicating the matching degree of the candidate person and the target post, wherein N indicates the number of primary capacity factors,/or%>The weight of the ith primary capability factor is represented, M represents the number of all secondary capability factors corresponding to the ith primary capability factor, +.>A weight representing a j-th secondary capability factor corresponding to the i-th primary capability factor, c ij And (5) representing the evaluation result of the j-th secondary capacity factor corresponding to the i-th primary capacity factor of the candidate person.
Further, the human enterprise matching model takes the obtained capacity factors and the corresponding weights as model parameters, and can generate a matching degree score for measuring the adaptation degree between the candidate personnel and the target post by taking the evaluation result of the candidate personnel on the corresponding capacity factors as input. It will be appreciated that the core task of the human enterprise matching model provided by the present invention is to compare the capabilities of the candidate personnel with the post requirements to determine the best match.
S05, acquiring an evaluation result of the capacity factor by the candidate personnel.
The method provided by the invention is not only suitable for calculating the target post matching degree of a single candidate person, but also suitable for calculating the target post matching degree of a plurality of candidate persons.
Further, the step S05 of obtaining the evaluation result of the capacity factor by the candidate person includes the following steps:
and S051, formulating corresponding evaluation standards according to the capacity factors in the human enterprise matching model.
In this embodiment, the evaluation criteria are defined for the candidate to evaluate. These criteria may be quantitative (e.g., skill level or certification) or descriptive (e.g., work experience description).
S052, creating an evaluation table or questionnaire according to the evaluation standard.
And creating an evaluation form or an online questionnaire according to the defined capacity factors and the evaluation standards. These tools will be used to record the evaluation results of the candidate.
S053, collecting feedback results of the evaluation list or the questionnaire, and acquiring evaluation results of the candidate personnel on the capacity factors according to the feedback results.
Further, the evaluation form or questionnaire is sent to the candidate, asking them to evaluate their own competence factors according to the evaluation criteria. This may be done through interviews, on-line surveys, or other evaluation means.
S06, combining the human enterprise matching model and the evaluation result to obtain the matching degree of the candidate personnel and the target post.
It can be understood that step S06 calculates the matching degree of each candidate person with the target post by combining the evaluation result of the candidate person with the requirement information of the target post. This match score represents the degree of adaptation between the candidate person and the post for comparison of the differences between the candidate persons. The recruiter of the target post can select the corresponding candidate person according to the matching degree of the candidate person and the target post, and similarly, the candidate person can select the working post suitable for the candidate person according to the matching degree.
In still another or some alternative embodiments, for matching a person enterprise of a plurality of candidate persons, in order to more screen candidate persons more suitable for an enterprise target, as shown in fig. 1, the method for matching a person talent with an enterprise supply and demand further includes the following steps:
s07, sorting the sizes of the matching degrees of the plurality of candidate persons and the target post.
It will be appreciated that the plurality of candidate persons are ordered from high to low in their degree of matching. In this way, it can be seen more clearly which alternatives have a higher degree of match with the target post.
S08, setting a distinguishing threshold, and extracting candidate personnel evaluation results with matching degree differences which do not meet the distinguishing threshold from the sorting results according to the distinguishing threshold.
When the degree of matching between the candidate persons is very close, the ranking obtained in step S07 may not be sufficient to explicitly distinguish their abilities and fitness.
Step S08 introduces a discrimination threshold for finer screening of candidates. If the difference in degree of match between the preceding candidate persons is smaller than the set threshold, their degree of match is very close, making it difficult to determine explicitly who is more appropriate. By extracting the candidates for which these differences do not meet the threshold, the number of candidates that need to be considered further can be reduced.
S09, based on the capability factor evaluation index system, after eliminating the evaluation result with the minimum first-level capability factor weight, updating the matching degree of the corresponding candidate personnel, and re-sequencing the updated matching degree until the matching degree difference of any two candidate personnel in the sequencing result meets the distinguishing threshold value.
In this embodiment, the updated matching degree satisfies the following model: wherein R is ′ ε Indicating the matching degree of the candidate person and the target post, N ′ Representing the number of remaining primary capacity factors, N ′ < N, N represents the number of primary capacity factors in the original capacity factor evaluation index system, < ->The weight of the ith primary capability factor is represented, M represents the number of all secondary capability factors corresponding to the ith primary capability factor, +.>A weight representing a j-th secondary capability factor corresponding to the i-th primary capability factor, c ij And (5) representing the evaluation result of the j-th secondary capacity factor corresponding to the i-th primary capacity factor of the candidate person.
It can be understood that the embodiment concentrates on evaluating the candidate persons more meeting the target post requirements in terms of the primary capability factors by eliminating the evaluation result with the minimum primary capability factor weight. This ensures that the candidate person not only performs well in overall match, but also performs well in core primary capacity factors.
Referring to fig. 2, in an alternative embodiment, to be able to efficiently perform a method for matching talents with supply and demand of an enterprise provided by the present invention, the present invention further provides a system for matching talents with supply and demand of an enterprise, where the system includes a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is configured to store a computer program, where the computer program includes program instructions, and where the processor is configured to invoke the program instructions to perform the specific steps of the embodiment related to the method for matching talents with supply and demand of an enterprise provided by the present invention.
It will be appreciated that the processor is the central computing unit of the system, responsible for executing the various computing tasks and instructions. Further, the processor is configured to invoke and execute a computer program associated with a talent and enterprise supply and demand matching method. The programs comprise tasks such as data preprocessing, capability factor weight calculation, matching degree calculation, alternative personnel sorting and the like.
The input device is used for receiving and inputting related data and information. In particular, the input device may comprise a keyboard, mouse, touch screen or other data input device. These devices can be used by a user (business/application) to provide demand information for a target job, evaluation results for alternative persons, and other necessary input data.
The output device is used for presenting the result of the system and output information to a user. In the present system, the output device may be a display screen, a printer, or other display and output device. The system may display the matching degree ranking of the candidate persons, the matching results, and other relevant information to the user via an output device.
The memory is used for storing data and computer programs required by the system. This includes input data, calculation intermediate results, weight calculation matrices, evaluation rules, and other necessary information. Specifically, the memory may be a hard disk drive, a solid state disk, a RAM (random access memory), or the like.
The system for matching talents with enterprise supply and demand has complete, objective and stable structure, and can efficiently execute the method for matching talents with enterprise supply and demand, thereby improving the overall applicability and practical application capability of the system.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention 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 or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (10)
1. A method for matching talents with supply and demand of an enterprise, comprising the steps of:
acquiring the demand information of an enterprise target post, and preprocessing the demand information;
extracting capacity factors required by a target post according to the requirement information;
setting the weight corresponding to the capacity factor;
combining the capacity factors and the corresponding weights to construct a human enterprise matching model;
obtaining an evaluation result of the capacity factor by the candidate personnel, wherein the number of the candidate personnel comprises one or more than one;
and combining the human-enterprise matching model and the evaluation result to obtain the matching degree of the candidate personnel and the target post.
2. The method for matching talents with supply and demand of enterprises according to claim 1, wherein the step of setting the weight corresponding to the capacity factor comprises the following steps:
according to the demand information, constructing a capability factor evaluation index system of a target post, wherein the capability factor evaluation index system comprises two or more capability factors;
based on the capability factor evaluation index system, evaluating the importance of each capability factor;
constructing a capacity factor weight calculation matrix according to the evaluation result;
and setting the weight of the capacity factor through the capacity factor weight calculation matrix.
3. The method for matching talents with supply and demand of enterprises according to claim 2, wherein the constructing a capability factor evaluation index system of a target post according to the demand information comprises the following steps:
aiming at the requirement information of a target post, dividing all capacity factors into primary capacity factors and secondary capacity factors;
and constructing a capability factor evaluation index system of the target post through the primary capability factors and the secondary capability factors, wherein the capability factor evaluation index system comprises a plurality of primary capability factors, and any one of the primary capability factors comprises one or more secondary capability factors.
4. The method for matching talents with supply and demand of enterprises according to claim 2, wherein the evaluation of importance of each capability factor based on the capability factor evaluation index system comprises the following steps:
extracting each level of capability factors in the capability factor evaluation index system to respectively obtain capability factor vectors of corresponding levels;
for the capability factor vector of the kth level, the importance (a) of one capability factor relative to the other capability factor in the capability factor vector is respectively evaluated according to an evaluation rule i ,a j ) The evaluation rule is as follows: on the target post, when the capacity factor a i And capability factor a j At the same critical scale, then (a i ,a j ) =0; when the ability factor a i Specific energy factor a j One important dimension higher, then (a i ,a j ) =0.01; when the ability factor a i Specific energy factor a j Two of the main importanceScale, then (a) i ,a j ) =0.02; when the ability factor a i Specific energy factor a j Three important dimensions higher, then (a) i ,a j ) =0.03; when the ability factor a i Specific energy factor a j Four important dimensions higher, then (a) i ,a j ) =0.04; when the ability factor a i Specific energy factor a j Five important dimensions higher, then (a) i ,a j ) =0.05; when the ability factor a i Specific energy factor a j One important dimension lower, then (a) i ,a j ) -0.01; when the ability factor a i Specific energy factor a j Two important dimensions lower, then (a) i ,a j ) -0.02; when the ability factor a i Specific energy factor a j Three important dimensions lower, then (a) i ,a j ) -0.03; when the ability factor a i Specific energy factor a j Four important dimensions lower, then (a) i ,a j ) -0.04; when the ability factor a i Specific energy factor a j Five important dimensions lower, then (a) i ,a j )＝-0.05。
5. The method for matching talents with supply and demand of enterprise according to claim 4, wherein the constructing the capacity factor weight calculation matrix according to the evaluation result comprises the following steps:
aiming at any level of capacity factors, constructing a capacity factor weight calculation matrix by combining the importance of the capacity factors with the corresponding level capacity factor vector, wherein the capacity factor weight calculation matrix G corresponding to the kth level capacity factor vector k The following model is satisfied:wherein (a) 1 ,a 1 )＝0，(a 1 ,a 1 ) Representing the importance of the 1 st capability factor in the k-th capability factor vector compared to the 1 st capability factor, (a) 1 ,a m ) Representing the importance of the 1 st capability factor in the k-th capability factor vector compared to the m-th capability factor, (a) m ,a 1 ) Representing the importance of the mth capability factor in the kth capability factor vector compared to the 1 st capability factor, (a) m ,a m )＝0，(a m ,a m ) The importance of the mth capability factor in the kth capability factor vector compared to the mth capability factor is represented.
6. The method for matching talents with enterprise supply and demand according to claim 5, wherein the step of obtaining the weight of any one capacity factor with respect to all capacity factors through the capacity factor weight calculation matrix comprises the following steps:
according to the capacity factor weight calculation matrix and the corresponding evaluation model, evaluating the importance degree of any capacity factor, wherein the evaluation model corresponding to the kth-level capacity factor vectorThe following formula is satisfied:wherein (1)>Representing the weight of the ith capability factor in the kth level capability factor vector, m representing the number of all capability factors in the kth level capability factor vector.
7. The method for matching talents with enterprise supply and demand according to claim 3, wherein the matching model of the talents and the enterprise satisfies the following formula:wherein R is ε Indicating the matching degree of the candidate person and the target post, wherein N indicates the number of primary capacity factors,/or%>Weights representing the ith level one capability factorWeight, M represents the number of all secondary capacity factors corresponding to the ith primary capacity factor, +.>A weight representing a j-th secondary capability factor corresponding to the i-th primary capability factor, c ij And (5) representing the evaluation result of the j-th secondary capacity factor corresponding to the i-th primary capacity factor of the candidate person.
8. The method for matching talents with supply and demand of an enterprise according to claim 3, further comprising the steps of:
sorting the matching degree of a plurality of candidate persons according to the size;
setting a distinguishing threshold, and extracting candidate personnel evaluation results corresponding to the distinguishing threshold from the sorting results according to the distinguishing threshold, wherein the matching degree difference does not meet the distinguishing threshold;
and based on the capability factor evaluation index system, removing the evaluation result with the minimum first-level capability factor weight, updating the matching degree of the corresponding candidate personnel, and re-sequencing the updated matching degree until the matching degree difference of any two candidate personnel in the sequencing result meets the distinguishing threshold value.
9. The method for talent matching with enterprise supply and demand of claim 8, wherein the updated matching degree satisfies the following model:wherein R is ′ ε Indicating the matching degree of the candidate person and the target post, N ′ Representing the number of remaining primary capacity factors, N ′ < N, N represents the number of primary capacity factors in the original capacity factor evaluation index system, < ->The weight of the ith primary capability factor is represented, M represents the corresponding ith primary capability factorThe number of all secondary capacity factors, +.>A weight representing a j-th secondary capability factor corresponding to the i-th primary capability factor, c ij And (5) representing the evaluation result of the j-th secondary capacity factor corresponding to the i-th primary capacity factor of the candidate person.
10. A system for talent to business supply and demand matching, the system comprising a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform a talent to business supply and demand matching method as claimed in any one of claims 1 to 9.
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