CN111144723A - Method and system for recommending people's job matching and storage medium - Google Patents
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Abstract
The invention discloses a method and a system for recommending the matching of the human sentry and a storage medium, wherein the method comprises the following steps: acquiring job text information input by a person unit and resume text information input by job seekers, wherein the job text information is related information aiming at positions to be recruited, and the resume text information is a resume of the job seekers; performing text analysis on the job text information and the resume text information based on a text analysis algorithm to obtain label information; and processing the label information by combining the job similarity algorithm and the job resume matching algorithm, and recommending the most matched resume for the current job. By combining various artificial intelligence algorithms, the method and the system can solve the problem of slow pain point search through pure manual operation, and improve the matching efficiency of the post matching and the matching degree of the final recommended resume.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a system for recommending post matching and a storage medium.
Background
The traditional recruitment service field is a business process that a recruitment advisor searches for a suitable candidate through position information and recommends the candidate to an enterprise, the candidate enters the position after resume screening and interview assessment of the enterprise, and the enterprise withdraws money for the recruitment advisor. There are certain problems in the process of counselor matching job and resume at present: firstly, how to quickly find a suitable candidate through the position information, particularly for a newly-entered recruiter, a long time is needed to master related experiences when the newly-entered recruiter faces a strange field; secondly, the existing recommendation system for plain text matching is difficult to infer the job seeking willingness of the candidate from the resume information, and is easily in the embarrassment that the job resume matching degree is high, but the candidate does not want to jump the slot, so that the trouble is wasted. In general, the prior art means has low efficiency and low matching degree when the method is used for post matching.
Disclosure of Invention
The embodiment of the invention provides a method and a system for recommending the people's post matching and a storage medium, which can improve the efficiency and the matching degree of the people's post matching.
The first aspect of the embodiments of the present invention provides a method for recommending a post match, which may include:
acquiring job text information input by a person unit and resume text information input by job seekers, wherein the job text information is related information aiming at a job to be recruited, and the resume text information is a resume of the job seekers;
performing text analysis on the position text information and the resume text information based on a text analysis algorithm to obtain label information;
and processing the label information by combining a job similarity algorithm and a job resume matching algorithm, and recommending the most matched resume for the current job.
A second aspect of the embodiments of the present invention provides a human-job matching recommendation system, which may include:
the system comprises a text acquisition module, a job position display module and a job seeker display module, wherein the text acquisition module is used for acquiring job position text information input by a person unit and resume text information input by job seekers, the job position text information is related information aiming at positions to be recruited, and the resume text information is a resume of the job seekers;
the text analysis module is used for performing text analysis on the job text information and the resume text information based on a text analysis algorithm to obtain label information;
and the post matching module is used for processing the label information by combining the job similarity algorithm and the job resume matching algorithm and recommending the most matched resume for the current job.
A third aspect of embodiments of the present invention provides a computer device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the method for recommending a human job matching according to the above aspect.
A fourth aspect of the embodiments of the present invention provides a computer storage medium, where at least one instruction, at least one program, a code set, or a set of instructions is stored in the computer storage medium, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the method for recommendation of human job matching according to the above aspect.
In the embodiment of the invention, the information related to the positions and the resumes acquired by the system is analyzed by introducing an artificial intelligence algorithm, the analyzed information is processed by combining the position similarity and the resume matching algorithm, and the most matched resume is recommended for the current position, wherein feedback information aiming at the candidate resumes is introduced before the post matching. By combining various artificial intelligence algorithms, the problem of slow searching of pain points by pure manual operation is solved, and the matching efficiency of the post matching and the matching degree of the final recommended resume are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for recommending a people's job matching according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating job classification according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of high-frequency keyword extraction according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of skill keyword extraction provided by the embodiment of the present invention;
FIG. 5 is a flow diagram of industry tag segmentation provided by embodiments of the present invention;
FIG. 6 is a schematic flow chart of required operating life identification provided by an embodiment of the present invention;
FIG. 7 is a flow chart illustrating the required academic recognition provided by the embodiment of the present invention;
FIG. 8 is a schematic flow chart of salary prediction provided by an embodiment of the present invention;
FIG. 9 is a flowchart illustrating a process for calculating similarity between a job and other jobs under the same secondary category according to an embodiment of the present invention;
FIG. 10 is a schematic flow chart illustrating job resume matching according to an embodiment of the present invention;
FIG. 11 is a flowchart illustrating another method for recommending a post match according to an embodiment of the present invention;
FIG. 12 is a schematic structural diagram of a human-job matching recommendation system according to an embodiment of the present invention;
fig. 13 is a schematic structural diagram of a human sentry matching module according to an embodiment of the present invention;
fig. 14 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "including" and "having," and any variations thereof, in the description and claims of this invention and the above-described drawings are intended to cover a non-exclusive inclusion, and the terms "first" and "second" are used for distinguishing designations only and do not denote any order or magnitude of a number. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that the method for recommending post matching provided by the application can be applied to an application scenario in which a hunting counselor screens suitable candidates according to the recruitment position of an enterprise and recommends the candidates to the enterprise.
In the embodiment of the invention, the method for recommending the post matching can be applied to computer equipment, and the computer equipment can be a computer or a smart phone and can also be other electronic equipment with computing processing capacity.
As shown in fig. 1, the method for recommending the post matching at least comprises the following steps:
s101, acquiring job position text information input by a person unit and resume text information input by job seekers.
It is understood that the job text information may be related information for a job to be recruited, which is entered by a person unit (i.e., an enterprise that needs to recruit employees) on the matching system or other recruitment website of the present application, and may include, for example, a job name of the job to be recruited, a recruitment requirement, and basic information (e.g., a scholarly, an age, a work place, a salary condition, etc.). The resume text information can be resumes uploaded by job seekers on the system or other recruitment websites, and can include job names, job experiences, skills and basic information of job seekers, and the like. Alternatively, the job text message and resume text message may be manually entered into the system by a recruiter or a hunting head or linked to the system from another website.
And S102, performing text analysis on the position text information and the resume text information based on a text analysis algorithm to obtain label information.
It is understood that the text parsing algorithm may split the job text information and the resume text information into a plurality of label information, including but not limited to job function classification, high frequency keywords, skill keywords, industry segmentation, salary prediction, required working years of the job, required academic records of the job, and the like.
In specific implementation, the process of parsing the tag information by the text parsing algorithm is as follows:
1) the process for job function classification in job text information and resume text information may be as shown in fig. 2: the method comprises the steps of respectively extracting the job names in the text, then classifying the job functions, and then storing classification results into a database.
In specific implementation, the system can classify positions and functions through three steps: pre-training a position and function classification model; acquiring job title information in the job text information and the resume text information; and putting the acquired job name information into the classification model for matching, and outputting a classification result. Wherein, the position text information resources in the system can be utilized in the process of carrying out the classification model training, and the TFIDF algorithm and the low-frequency word filtering are combined, b, calculating mutual information of the bi-gram and manually integrating and sorting a 3-level position classification system (52 classification marks at the 1 level, 800 classification marks at the 2 level and more than 4000 classification marks at the 3 level), establishing a 3-level tier-corresponding 2-level tier tree by using the sorted classification marks (for example, 3-level identification java development and java background development both belong to 2-level identification java development, and the tier tree relationship is { j- > a- > v- > a- > research- > a- > launch- > java development } and { j- > a- > v- > a- > rear- > platform- > launch- > java development), and then storing the established relationship into a tier tree structure. Further, when the acquired job name information is put into the classification model for matching and a classification result is output, the system can start comparison from existing characters, and output the result by a greedy algorithm at the ending part, such as job name "development of deep java backend", because the word "resource" does not exist in the initial lookup list of the tier tree, the system skips over from j to { j- > a- > v- > a- > back }, stops at the end of the word "back", and the ending word is located in { j- > a- > v- > a- > back- > station- > open- > development } so as to output the matching classification result as java development and store the result in the database.
2) The extraction process for high frequency keywords may be as shown in fig. 3: extracting work experience and project experience in resume text information, and extracting job description and job requirements in job text information; and extracting high-frequency keywords according to the extracted data, and then putting the keyword extraction result into a database.
In specific implementation, the system can extract the high-frequency keywords through three steps: respectively acquiring job description and job requirements in job text information, acquiring work experience and project experience in resume text information, and performing word segmentation processing on the acquired information; then comprehensively judging the key degree of a single word through several characteristics of word frequency, word property and semantic relevance of the word segmentation result; and finally, sorting the scores of all the words from high to low and storing the scores into a database. It should be noted that the score of each word reflects the degree of criticality of the word, and preferably, the ratio of the word frequency, the part of speech and the semantic relevance may be 40%, 10% and 50% respectively. Wherein, the TFIDF algorithm can be used to replace the traditional word frequency. It can be understood that the word importance judgment through the part of speech is a common method, and can well process partial conditions, for example, words without practical meaning such as a particle word, a quantity word and the like can be given a very low score, and user-defined words, English words and the like can be given a very high score. It should be noted that semantic relevance refers to the overall relevance of the word to other words in the whole text.
3) The extraction process for the skill keyword may be as shown in fig. 4: the method comprises the steps of pre-training a skill keyword extraction model; respectively acquiring job description and job requirements in the job text information, and acquiring work experience and project experience in the resume text information; and putting the obtained data into a keyword extraction model for calculation and outputting a result to a database.
4) The subdivision process for subdividing industry tags may be as shown in FIG. 5: pre-training an industry label system based on all position text information and resume text information in the system; acquiring relevant information of companies (such as company description and company main operation) by analyzing the company to which the job belongs and the company to which the resume work experience belongs; and putting the relevant information of the company into a subdivision industry label system to calculate a classification label of a first-level subdivision industry and a classification label of a second-level subdivision industry, and storing the classification labels into a database.
5) The identification process for the required working years for a position may be as shown in fig. 6: the method comprises the steps of extracting the job requirements in the job text information and further identifying the working years required by the job. Preferably, the system may extract the minimum working years and the maximum working years required by the position by using a regular matching formula, for example, the working experience requiring more than three years is extracted into the minimum working years 3 and the maximum working years 99.
6) The identification process for the required academic records of the job can be as shown in fig. 7: the method comprises the steps of extracting job requirements in job text information and further identifying a study required by the job. Preferably, the system identifies the minimum subject required by the job by using a regular matching formula, for example, the subject above the subject is extracted as the subject of the minimum subject.
7) The prediction process for salary prediction may be as shown in fig. 8: the method comprises the steps of pre-training a salary prediction model; judging whether the resume text information is filled with salary requirements or not, if so, directly storing the resume text information into a database, and if not, acquiring the label information and the basic information of the resume from the database; and calculating the predicted salary corresponding to the acquired label information and the basic information based on the salary prediction model, and storing the predicted salary into a database.
It should be noted that, when the salary prediction model is trained, the resume can be analyzed into the label information by using a text analysis algorithm in the system, then the basic information (such as a working city, an age and the like) of the resume stored in the database is added, the salary hierarchical model is established by combining with the xgboost algorithm, and further, the algorithm model for salary prediction is established on the basis of the hierarchical model by using the ridge regression algorithm. When the predicted salary corresponding to the acquired label information and the basic information is calculated based on the salary prediction model, the information can be judged whether the salary is high salary/medium salary through a salary grading model, and then the corresponding salary prediction model is called to calculate the predicted salary.
In an alternative implementation, after the resume is recommended to the user unit, the recommended candidate resume can be subjected to the following situations: selected or unselected resumes or not taken after interviewing, etc. In view of the above, the hunting consultant can consult the user unit manually and then add the resume status information to the system, and further, the system can add new label information to the corresponding resume text information based on the resume status information. For example, company A interviews candidate X before enrollment, and the advisor may add the reason for the enrollment as a new label for the resume. Through enriching the label information of the resume, the probability of successful matching of the follow-up post is increased.
In the embodiment of the application, when the hunting consultant logs in the system to perform the relevant operation, the system will firstly confirm whether the hunting consultant is a value-added member, if so, the hunting consultant directly accesses the system, and if not, the hunting consultant only can access other functions of the website.
And S103, processing the label information by combining the job similarity algorithm and the job resume matching algorithm, and recommending the most matched resume for the current job.
It should be noted that the job similarity is the similarity between similar jobs recruited by different people. In specific implementation, the system can calculate a similarity value between the position tags in any two position text messages by adopting a position similarity algorithm, and the similarity value is used as a similarity value between the two position text messages, wherein the position tags are tags related to positions in the tag information. Further, the system may sort the similar job text information according to the size of the similarity value, and select a candidate resume corresponding to the TopN job most similar to the current job as the resume to be recommended, where N is a positive integer greater than or equal to 1.
Optionally, the system may further perform matching calculation on the job text information and the job labels and the basic information in the resume text information by using a job resume matching algorithm, and match a resume most suitable for the current job as a candidate resume. It is to be understood that the resume that best matches the current position may be the resume that matches the top TopN position with higher similarity to the current position after calculation.
In a preferred implementation, the system can classify the positions in the position text information when performing position similarity calculation, and store the positions into the database according to the classification of the secondary position classification identifiers.
Further, after the secondary class identifier is classified and stored, the system can perform vector dictionary pre-training. The specific training process is as follows: dividing all job text information in the system into words by a jieba word divider, wherein the job text information comprises job names, job descriptions and job requirements; and then combining the results after word segmentation, removing duplication and establishing a vector dictionary. For example, Xiaoming/like/eating/Ice cream/also/like/eating/chafing dish, Xiaoming/like/playing/Game two will establish vector dictionaries [ Xiaoming: 1, like: 2, eating: 3, Ice cream: 4, also: 5, chafing dish: 6, playing: 7, Game: 8], which are stored in the database.
Further, the system may calculate similarity between the position and other positions under the same secondary classification, and the process is shown in fig. 9 and specifically includes: a) performing position and function classification processing on the positions to be calculated to obtain secondary position and function identifiers; b) selecting all positions under the same secondary position function identifier in the result of the step a); c) segmenting words of all the positions obtained in the step b), vectorizing the segmentation results according to the pre-trained vector dictionary, and simultaneously vectorizing the positions to be calculated. Wherein, vectorization means that the word segmentation result is converted into a vector according to the number of times of occurrence of the word and the position of the word in the vector dictionary, for example, the result after the vectorization of xiaoming/like/eating/ice cream/also/like/eating/hot pot is [1,2,2,1,1,0,0,0 ]. d) Calculating the similarity of the position obtained in the step a) and the position to be calculated by using cosine vectors in sequence.
Further, the system can rank the calculation of the similarity from high to low, and the result is stored in the database.
In a preferred implementation, the process of the system for job resume matching is shown in fig. 10, and includes the following steps:
firstly, acquiring label information and basic information of a position in position text information and resume text information; then, the resume with non-compliant hard overrules, such as sex non-compliance, age non-compliance, background non-compliance, city non-compliance, etc., is removed. Further, in the resume meeting the hard requirement, combining the label information, calculating the position keyword matching score, including the skill keyword score of 9BM25 and the high-frequency keyword BM25, preferably, requiring at least 10% of the keywords to be matched. Further, in the resume meeting the hard requirement, the label information and the basic information are combined to calculate other information weighted scores, including whether the job position and function identifications are consistent, whether the working years are consistent, whether the subdivided industries are consistent, whether the salary range is included and the like. Further, the job keyword matching score and other information weighted scores are summarized, and a final job and resume matching score is calculated, wherein the specific formula is as follows: score5 ═ score3 × (score 4), where score5 is the total score of final job and resume matches, score3 is the job keyword match score, and score4 is the other information weighted score. Further, the calculation results can be sorted from high to low and stored in a database.
It should be noted that, in order to ensure timeliness of information, the data processing and algorithm execution processes in this embodiment all adopt real-time calculation.
It should be noted that the system may use a recommendation sorting algorithm to sort the calculation result of the job similarity algorithm and the matching result of the job resume matching algorithm based on the result of the preset priority. The preset priority can be result priority of the job resume matching algorithm, candidate list priority obtained by the job similarity algorithm, or proportion selection of the two. The recommendation sorting algorithm can adopt implementation calculation or timing calculation, and plays a role in elastic balance of timeliness and development cost of the algorithm.
In the embodiment of the invention, the information related to the positions and the resumes acquired by the system is analyzed by introducing an artificial intelligence algorithm, the analyzed information is processed by combining the position similarity and the resume matching algorithm, and the most matched resume is recommended for the current position, wherein feedback information aiming at the candidate resumes is introduced before the post matching. By combining various artificial intelligence algorithms, the problem of slow searching of pain points by pure manual operation is solved, and the matching efficiency of the post matching and the matching degree of the final recommended resume are improved.
Please refer to fig. 11, which is another flowchart of the method for recommendation of post matching in the present application, wherein the three-party data stored in the database in the flowchart is label information corresponding to the position, resume, and candidate status information, and the recruiting consultant needs to enter data into the system and receive the final recommendation result of the system as a person operating the system. The candidate status information entry in the figure is resume status information in the above method embodiment.
It should be noted that, for the specific implementation process of this embodiment, reference may be made to the detailed description of the method embodiment described above, and details are not described here again.
In the embodiment of the invention, the information related to the positions and the resumes acquired by the system is analyzed by introducing an artificial intelligence algorithm, the analyzed information is processed by combining the position similarity and the resume matching algorithm, and the most matched resume is recommended for the current position, wherein feedback information aiming at the candidate resumes is introduced before the post matching. By combining various artificial intelligence algorithms, the problem that slow pain points are searched due to pure manual operation is solved, and the matching working efficiency of the post and the matching degree of the final recommended resume are improved.
The following describes in detail a human-job matching recommendation system according to an embodiment of the present invention with reference to fig. 12. It should be noted that the human job matching recommendation system shown in fig. 12 is used for executing the method according to the embodiment of the present invention shown in fig. 1 to 11, for convenience of description, only the portion related to the embodiment of the present invention is shown, and details of the specific technology are not disclosed, please refer to the embodiment of the present invention shown in fig. 1 to 11.
Referring to fig. 12, a schematic structural diagram of a human sentry matching recommendation system is provided in the embodiment of the present invention. As shown in fig. 12, the human job matching recommendation system 10 according to the embodiment of the present invention may include: the system comprises a text acquisition module 101, a text analysis module 102, a human-sentry matching module 103, a state information acquisition module 104, a tag information adding module 105 and a result sorting module 106. As shown in fig. 13, the post matching module 103 includes a job similarity calculation unit 1031, a job text sorting unit 1032, and a job resume matching unit 1033.
The text acquisition module 101 is configured to acquire job position text information entered by a person-using unit and resume text information entered by a job seeker, where the job position text information is related information for a job position to be recruited, and the resume text information is a resume of the job seeker.
And the text analysis module 102 is configured to perform text analysis on the position text information and the resume text information based on a text analysis algorithm to obtain tag information.
In an optional embodiment, the status information acquiring module 104 is configured to acquire the input resume status information, where the resume status information is feedback information given by a person unit for candidate resumes at the recruitment position of the person unit.
Further, the tag information adding module 105 is configured to add new tag information to the corresponding resume text information based on the resume state information.
In the embodiment of the application, the label information at least comprises one or more of job function classification, high-frequency keywords, skill keywords, industry subdivision, salary prediction, required working years of the job and required academic calendar of the job.
And the post matching module 103 is used for processing the label information by combining the job similarity algorithm and the job resume matching algorithm and recommending the most matched resume for the current job.
In an alternative embodiment, the people-post matching module 103 comprises:
and a job similarity calculation unit 1031 configured to calculate a similarity value between job labels in any two job text messages as a similarity value between the two job text messages by using a job similarity algorithm.
And the job text sorting unit 1032 is used for sorting similar job text information according to the size of the similarity numerical value.
And a resume matching unit 1033, configured to perform matching calculation on the job labels and the basic information in the job text information and the resume text information by using a job resume matching algorithm, and match a resume that best matches the current job, where the basic information is other basic information except the label information in the job text information and the resume text information.
It should be noted that the job text information and the resume text information both include job labels associated with the jobs.
In an optional embodiment, the result sorting module 106 is configured to sort, by using a recommendation sorting algorithm, the calculation result of the job similarity algorithm and the matching result of the job resume matching algorithm based on a preset priority.
It should be noted that, for the execution process of each module and unit in this embodiment, reference may be made to the description in the foregoing method embodiment, and details are not described here again.
In the embodiment of the invention, the information related to the positions and the resumes acquired by the system is analyzed by introducing an artificial intelligence algorithm, the analyzed information is processed by combining the position similarity and the resume matching algorithm, and the most matched resume is recommended for the current position, wherein feedback information aiming at the candidate resumes is introduced before the post matching. By combining various artificial intelligence algorithms, the problem of slow searching of pain points by pure manual operation is solved, and the matching efficiency of the post matching and the matching degree of the final recommended resume are improved.
An embodiment of the present invention further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing the method steps in the embodiments shown in fig. 1 to 11, and a specific execution process may refer to specific descriptions of the embodiments shown in fig. 1 to 11, which are not described herein again.
The embodiment of the application also provides computer equipment. As shown in fig. 14, the computer device 20 may include: the at least one processor 201, e.g., CPU, the at least one network interface 204, the user interface 203, the memory 205, the at least one communication bus 202, and optionally, a display 206. Wherein a communication bus 202 is used to enable the connection communication between these components. The user interface 203 may include a touch screen, a keyboard or a mouse, among others. The network interface 204 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), and a communication connection may be established with the server via the network interface 204. The memory 205 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory, and the memory 205 includes a flash in the embodiment of the present invention. The memory 205 may optionally be at least one memory system located remotely from the processor 201. As shown in fig. 14, the memory 205, which is a type of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and program instructions.
It should be noted that the network interface 204 may be connected to a receiver, a transmitter or other communication module, and the other communication module may include, but is not limited to, a WiFi module, a bluetooth module, etc., and it is understood that the computer device in the embodiment of the present invention may also include a receiver, a transmitter, other communication module, etc.
acquiring job text information input by a person unit and resume text information input by job seekers, wherein the job text information is related information aiming at positions to be recruited, and the resume text information is a resume of the job seekers;
performing text analysis on the job text information and the resume text information based on a text analysis algorithm to obtain label information;
and processing the label information by combining the job similarity algorithm and the job resume matching algorithm, and recommending the most matched resume for the current job.
In some embodiments, the tag information includes at least one or more of job function categories, high frequency keywords, skill keywords, segment industry, salary forecasts, required working years for the job, required scholarly for the job.
In some embodiments, the apparatus 20 is further configured to obtain the input resume state information, which is feedback information given by the employment unit for the candidate resumes at the recruitment position of the unit;
and adding new label information for the corresponding resume text information based on the resume state information.
In some embodiments, the device 20 processes the label information in combination with the job similarity algorithm and the job resume matching algorithm, and when recommending the most matching resume for the current job, is specifically configured to:
calculating a similarity value between the job labels in any two job text messages by using a job similarity algorithm to serve as a similarity value between the two job text messages;
sequencing the similar job text information according to the size of the similarity numerical value;
and matching and calculating the position labels and the basic information in the position text information and the resume text information by adopting a position resume matching algorithm to obtain a resume which best meets the current position, wherein the basic information is other basic information except the label information in the position text information and the resume text information.
In some embodiments, the job label associated with the job is included in both the job text information and the resume text information.
In some embodiments, the apparatus 20 further comprises sorting the calculated results of the job similarity algorithm and the matching results of the job resume matching algorithm using a recommendation sorting algorithm based on a preset priority.
In the embodiment of the invention, the information related to the positions and the resumes acquired by the system is analyzed by introducing an artificial intelligence algorithm, the analyzed information is processed by combining the position similarity and the resume matching algorithm, and the most matched resume is recommended for the current position, wherein feedback information aiming at the candidate resumes is introduced before the post matching. By combining various artificial intelligence algorithms, the problem of slow searching of pain points by pure manual operation is solved, and the matching efficiency of the post matching and the matching degree of the final recommended resume are improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A method for recommending the matching of the human sentry is characterized by comprising the following steps:
acquiring job text information input by a person unit and resume text information input by job seekers, wherein the job text information is related information aiming at a job to be recruited, and the resume text information is a resume of the job seekers;
performing text analysis on the position text information and the resume text information based on a text analysis algorithm to obtain label information;
and processing the label information by combining a job similarity algorithm and a job resume matching algorithm, and recommending the most matched resume for the current job.
2. The method of claim 1, wherein the label information comprises one or more of job function category, high frequency keywords, skills keywords, segment industry, salary forecast, required working years for a job, required scholarly for a job.
3. The method of claim 1, further comprising:
acquiring the input resume state information, wherein the resume state information is feedback information given by the employing unit for the candidate resumes under the recruitment position of the unit;
and adding new label information for the corresponding resume text information based on the resume state information.
4. The method of claim 1, wherein the processing the label information in conjunction with job similarity algorithms and job resume matching algorithms to recommend a best matching resume for the current job comprises:
calculating a similarity value between the job labels in any two job text messages by using a job similarity algorithm to serve as a similarity value between the two job text messages;
sorting the similar job text information according to the size of the similarity numerical value, and selecting a candidate resume corresponding to the TopN job most similar to the current job, wherein N is a positive integer greater than or equal to 1;
and/or matching and calculating the job labels and the basic information in the job text information and the resume text information by adopting a job resume matching algorithm to match and obtain the resume which best meets the current job, wherein the basic information is the job text information and other information except the label information in the resume text information.
5. The method of claim 4, wherein the job label is a label associated with a job in the label information.
6. The method of claim 4, further comprising:
and adopting a recommendation sorting algorithm to sort the calculation result of the job similarity algorithm and the matching result of the job resume matching algorithm based on a preset priority.
7. A human-job matching recommendation system, comprising:
the system comprises a text acquisition module, a job position display module and a job seeker display module, wherein the text acquisition module is used for acquiring job position text information input by a person unit and resume text information input by job seekers, the job position text information is related information aiming at positions to be recruited, and the resume text information is a resume of the job seekers;
the text analysis module is used for performing text analysis on the job text information and the resume text information based on a text analysis algorithm to obtain label information;
and the post matching module is used for processing the label information by combining the job similarity algorithm and the job resume matching algorithm and recommending the most matched resume for the current job.
8. The system of claim 7, wherein the label information includes one or more of job function category, high frequency keywords, skills keywords, segment industry, salary forecast, required working years for a job, required scholarly for a job.
9. The system of claim 7, further comprising:
the state information acquisition module is used for acquiring the input resume state information, and the resume state information is feedback information given by the employment unit for the candidate resumes at the recruitment position of the employment unit;
and the label information adding module is used for adding new label information to the corresponding resume text information based on the resume state information.
10. A computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the method of claim 1 to 6.
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