A big data-based intelligent recruitment and employment recommendation system

Abstract

The application discloses a kind of based on big data's recruitment employment intelligent recommendation system, belong to computer technology field, the technical scheme provided by the application embodiment proposes a multi-level, multi-dimensional intelligent matching system.The system is through the screening process of three stages, i.e.industry matching, enterprise matching and position matching, gradually narrow the scope of candidate, finally recommend the most suitable job seeker.Each stage is set with matching degree threshold, to ensure the high quality of recommended result.In addition, the system makes full use of big data, not only considers the skills and experience of job seeker, but also includes industry characteristics, enterprise culture and specific position requirements and other factors, realizes more comprehensive, more accurate matching.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a recruitment and employment intelligent recommendation system based on big data. BACKGROUND

[0002] Most traditional recruitment and employment platforms mainly rely on job seekers to input resume information and enterprises to publish job position information. When these platforms perform matching, they often only match based on simple keywords, such as matching the position name with the work experience name in the resume. Some platforms have some basic screening functions, such as screening by education level and work experience, but the dimensions of these screenings are relatively limited and cannot deeply explore the potential matching degree between job seekers and positions.

[0003] Due to the simple matching algorithm of existing recruitment and employment platforms, a large number of job seekers and positions are not matched. For example, a job seeker with rich project management experience but with a mismatched position name may be missed, and the enterprise may miss the right talent.

[0004] Therefore, how to recommend talents with high matching degree to enterprises is a research hotspot. SUMMARY

[0005] The embodiments of the present application provide a recruitment and employment intelligent recommendation system based on big data, which can improve the matching degree between recommended talents and enterprises, and can improve efficiency compared with manual screening in the prior art. The technical scheme is as follows:

[0006] A recruitment and employment intelligent recommendation system based on big data is provided, and the system comprises:

[0007] An acquisition module is configured to acquire object recruitment information of a target enterprise, object information of a plurality of initial objects, industry information of an industry where the target enterprise is located, and enterprise evaluation information of the target enterprise in response to an object recommendation request of the target enterprise, wherein the plurality of initial objects are objects recommended by a plurality of target object recommendation platforms, the plurality of target object recommendation platforms are object recommendation platforms with a credibility greater than or equal to a credibility threshold, the object recruitment information comprises enterprise information of the target enterprise and position information of a target position, and the target position is a position to be recommended for objects;

[0008] A reference object determination module is configured to determine a plurality of reference objects from the plurality of initial objects based on the object information of the plurality of initial objects and the industry information, wherein the reference objects are initial objects with a matching degree greater than or equal to a first matching degree threshold with the industry where the target enterprise is located;

[0009] a candidate object determination module configured to determine a plurality of candidate objects from the plurality of reference objects based on the enterprise information of the target enterprise, the enterprise evaluation information of the target enterprise, and the object information of the plurality of reference objects, the candidate object being a reference object with a matching degree greater than or equal to a second matching degree threshold with the target enterprise;

[0010] a target object determination module configured to determine at least one target object from the plurality of candidate objects based on the position information of the target position and the object information of the plurality of candidate objects, the target object being a reference object with a matching degree greater than or equal to a third matching degree threshold with the target position;

[0011] a recommendation module configured to recommend the at least one target object to the target enterprise.

[0012] The technical scheme provided by the embodiments of the present application proposes an intelligent matching system with multiple levels and multiple dimensions. The system gradually narrows down the candidate range through a three-stage screening process, i.e., industry matching, enterprise matching, and position matching, and finally recommends the most suitable job seeker. Each stage is provided with a matching degree threshold to ensure the high quality of the recommendation result. In addition, the system makes full use of big data, not only considering the skills and experience of the job seeker, but also taking into account the industry characteristics, enterprise culture, and specific position requirements and other factors, achieving more comprehensive and accurate matching, and compared with the manual screening in the prior art, the efficiency can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical scheme in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0014] Figure 1 is a schematic diagram of an implementation environment of an intelligent recruitment and employment recommendation system based on big data provided by the embodiments of the present application;

[0015] Figure 2 is a structural schematic diagram of an intelligent recruitment and employment recommendation system based on big data provided by the embodiments of the present application;

[0016] Figure 3 is a structural schematic diagram of a reference object determination module provided by the embodiments of the present application;

[0017] Figure 4 is a structural schematic diagram of a candidate object determination module provided by the embodiments of the present application;

[0018] Figure 5 is a structural schematic diagram of a target object determination module provided by an embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0020] In the present application, the terms "first", "second", and the like are used to distinguish the same or similar items with basically the same function, and it should be understood that there is no logical or time sequence dependency between "first", "second", and "nth", and the quantity and execution order are not limited.

[0021] Big data: Big data is a data set with huge size, complex types, and greatly exceeding the capability range of traditional database software tools in acquisition, storage, management, and analysis.

[0022] Artificial intelligence (AI) is the use of digital computers or machine controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain better results.

[0023] Machine learning (ML) is a multi-disciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It is a subject that studies how computers simulate or implement human learning behavior to acquire new knowledge or skills, reorganize existing knowledge sub-models to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental approach to making computers intelligent. Its applications are widespread in various fields of artificial intelligence. Machine learning and deep learning usually include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and teaching learning.

[0024] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in the present application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0025] Figure 1 is an implementation environment schematic diagram of an intelligent recruitment and employment recommendation method based on big data provided by an embodiment of the present application, referring to Figure 1 The implementation environment can include a first node 110, a second node 120 and a server 140.

[0026] The first node 110 is connected to the server 140 through a wireless network or a wired network. Optionally, the first node 110 is a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but is not limited thereto. The first node 110 is installed and runs an application program supporting intelligent recommendation of recruitment and employment based on big data. In the embodiment of the present application, the first node 110 is a node corresponding to a target object recommendation platform, and the server 140 can obtain object information of an initial object from the first node 110.

[0027] The second node 120 is connected to the server 140 through a wireless network or a wired network. Optionally, the second node 120 is a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but is not limited thereto. The second node 120 is installed and runs an application program supporting intelligent recommendation of recruitment and employment based on big data. In the embodiment of the present application, the second node 120 is a node corresponding to a target enterprise, and the server 140 can obtain object recruitment information of the target enterprise from the second node 120.

[0028] The server 140 is a stand-alone physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content delivery network (CDN), and big data and artificial intelligence platform. The server 140 is deployed with the intelligent recommendation system of recruitment and employment based on big data provided by the embodiment of the present application. The server 140 can provide background services for the application programs running on the first node 110 and the second node 120. In the embodiment of the present application, the server is also referred to as an online modeling platform.

[0029] In the traditional recruitment and employment platform, the matching algorithm is too simple, mainly relying on keyword matching and basic screening function. This method cannot deeply mine the potential matching degree between job seekers and positions, resulting in a large number of mismatching problems between job seekers and positions. Specifically, this simple matching method cannot consider the comprehensive ability of job seekers, the potential of career development, and the compatibility with enterprise culture, etc. Therefore, how to realize accurate matching between enterprises and job seekers based on big data has become a technical problem to be solved.

[0030] In a large internet recruitment platform, hundreds of thousands of resumes and tens of thousands of job postings are published every day. The platform uses a keyword-based matching algorithm, but its accuracy is only 30%. For example, a software engineer with rich project management experience is ignored by the system when matching for a project manager position because the keyword "project manager" does not appear in his resume. At the same time, some cross-industry job seekers are also difficult to be identified as potential matching objects by the system because their past work experience is different from the industry of the target position. In addition, the system cannot effectively consider soft factors such as corporate culture and work environment, resulting in the possibility that even if the skills of the job seeker match, he or she may still leave the job due to cultural inadaptation after being hired.

[0031] If this technical problem cannot be effectively solved, it will cause serious waste of talent resources and low efficiency of enterprise recruitment. Specifically, a large number of potential suitable talents may be ignored by the system, while unsuitable candidates may be recommended to the enterprise, increasing the screening cost of the enterprise. For job seekers, they may miss many suitable job opportunities, prolonging the job search period. From a technical point of view, this problem will also lead to a decline in user experience and user trust, which may ultimately affect the operation effect of the entire platform. Therefore, it is particularly important to develop an intelligent recommendation system that can analyze the matching degree of job seekers and positions in multiple dimensions and at a deep level.

[0032] In traditional recruitment platforms, the matching algorithm is too simple and mainly relies on keyword matching and basic screening functions, which cannot deeply explore the potential matching degree between job seekers and positions. This leads to a large number of job seekers and positions that do not match, affecting recruitment efficiency and job search success rate.

[0033] To solve this problem, this application explores a variety of possible solutions. First, consider adding more screening dimensions, such as education, work experience, and skills. However, this method, although it can improve matching accuracy, still cannot fully evaluate the matching degree of job seekers and positions.

[0034] Next, consider introducing artificial intelligence technologies such as natural language processing and machine learning algorithms for semantic analysis of resumes and job descriptions. This method can better understand the abilities and experiences of job seekers, but still lacks consideration of industry and enterprise characteristics.

[0035] After careful consideration, this application realizes the need for a multi-level and multi-dimensional matching system. This system should not only consider the personal information of job seekers, but also consider industry characteristics, corporate culture, and specific job requirements. Thus, an intelligent recruitment recommendation system based on big data is proposed.

[0036] Specifically, the system first acquires the object recruitment information of the target enterprise, the object information of multiple initial objects, the industry information of the industry in which the target enterprise is located, and the enterprise evaluation information of the target enterprise. Among them, the initial objects come from a high-reliability recommendation platform, ensuring the reliability of the data.

[0037] Then, the system gradually narrows down the candidate range through a multi-step screening process:

[0038] First, based on the information of the initial objects and the industry information, reference objects with high matching degree to the industry in which the target enterprise is located are screened out. This step ensures that the recommended job seekers match the enterprise in terms of industry background.

[0039] Second, based on the information and evaluation of the target enterprise, candidate objects with high matching degree to the enterprise are screened out from the reference objects. This step considers the characteristics of the enterprise, ensuring that the recommended job seekers can adapt to the enterprise culture and working environment.

[0040] Finally, based on the specific requirements of the target position, the final target object is determined from the candidate objects. This step ensures that the recommended job seekers not only match the industry and enterprise, but also meet the requirements of the specific position.

[0041] Through this multi-level screening process, the system can comprehensively consider the matching degree of job seekers with enterprises and positions, greatly improving the accuracy and efficiency of matching. At the same time, by setting multiple matching degree thresholds, the system can retain the most suitable candidates and eliminate unsuitable objects at each step of screening, further improving the quality of recommendations.

[0042] The following examples will introduce the big data-based recruitment and employment intelligent recommendation system (hereinafter referred to as the system) provided by the embodiments of the present application.

[0043] Embodiment 1

[0044] Figure 2 is a structural schematic diagram of a big data-based recruitment and employment intelligent recommendation system provided by the embodiments of the present application, referring to Figure 2 , the system 200 includes:

[0045] The acquisition module 201 is configured to acquire, in response to an object recommendation request of a target enterprise, object recruitment information of the target enterprise, object information of multiple initial objects, industry information of an industry in which the target enterprise is located, and enterprise evaluation information of the target enterprise. The multiple initial objects are objects recommended by multiple target object recommendation platforms, the multiple target object recommendation platforms are object recommendation platforms with a reliability greater than or equal to a reliability threshold, the object recruitment information includes enterprise information of the target enterprise and position information of a target position, and the target position is a position to be recommended.

[0046] The object gathering information refers to information published by the target enterprise for recruiting talents, and can be implemented by enterprise information and position information. The initial object refers to a potential job seeker recommended by a high-reliability recommendation platform, and can be implemented by personal information, work experience and education background of the job seeker. The industry information refers to relevant data of the industry in which the target enterprise is located, and can be implemented by industry development trend, skill demand and market prospect. The enterprise evaluation information refers to various evaluation data about the target enterprise, and can be implemented by employee satisfaction, company culture and working environment.

[0047] The reference object determination module 202 is configured to determine a plurality of reference objects from the plurality of initial objects based on the object information of the plurality of initial objects and the industry information, the reference object being an initial object with a matching degree with the industry in which the target enterprise is located greater than or equal to a first matching degree threshold.

[0048] The matching degree refers to the degree of fit between the job seeker and the enterprise or the position, and can be implemented by a multi-dimensional scoring system.

[0049] The candidate object determination module 203 is configured to determine a plurality of candidate objects from the plurality of reference objects based on the enterprise information of the target enterprise, the enterprise evaluation information of the target enterprise and the object information of the plurality of reference objects, the candidate object being a reference object with a matching degree with the target enterprise greater than or equal to a second matching degree threshold.

[0050] The target object determination module 204 is configured to determine at least one target object from the plurality of candidate objects based on the position information of the target position and the object information of the plurality of candidate objects, the target object being a reference object with a matching degree with the target position greater than or equal to a third matching degree threshold.

[0051] The recommendation module 205 is configured to recommend the at least one target object to the target enterprise.

[0052] The core innovation of the present application is to propose a multi-level and multi-dimensional intelligent matching system. The system gradually narrows down the candidate range through three stages of screening process, i.e. industry matching, enterprise matching and position matching, and finally recommends the most suitable job seeker. Each stage sets a matching degree threshold to ensure the high quality of the recommended result. In addition, the system makes full use of big data, not only considering the skills and experience of the job seeker, but also taking into account the industry characteristics, enterprise culture and specific position requirements and other factors, achieving more comprehensive and accurate matching.

[0053] The working principle of the present application can be described in detail as follows:

[0054] Firstly, the system acquires various types of information. Target gathering information is acquired from the target enterprise, including basic enterprise information and specific job requirements. Initial object information is acquired from high-reliability recommendation platforms, including the job seeker's personal information, work experience, and educational background, etc. Industry information is acquired from relevant databases or industry reports, including industry development trends and skill requirements, etc. Enterprise evaluation information is acquired from employee feedback, public evaluation, etc., including company culture and work environment evaluation, etc.

[0055] Then, the system performs three-stage screening:

[0056] The first stage is industry matching. The system uses initial object information and industry information to calculate the matching degree of each initial object with the industry of the target enterprise. The matching degree calculation may involve factors such as the job seeker's industry experience, relevant skills, etc. Only initial objects with a matching degree exceeding the first matching degree threshold value will be selected as reference objects.

[0057] The second stage is enterprise matching. The system uses target enterprise information, enterprise evaluation information, and reference object information to calculate the matching degree of each reference object with the target enterprise. This stage may consider factors such as corporate culture, work environment, etc. Only reference objects with a matching degree exceeding the second matching degree threshold value will be selected as candidate objects.

[0058] The third stage is position matching. The system uses target position information and candidate object information to calculate the matching degree of each candidate object with the target position. This stage may focus on factors such as specific skill requirements, work experience, etc. Only candidate objects with a matching degree exceeding the third matching degree threshold value will be finally selected as target objects.

[0059] Finally, the system recommends the screened target objects to the target enterprise. The first matching degree threshold value, the second matching degree threshold value, and the third matching degree threshold value are set by technical personnel according to actual conditions, and the embodiments of the present application do not limit them.

[0060] The design of this multi-stage screening makes the system able to comprehensively consider the matching degree of job seekers with enterprises and positions, greatly improving the accuracy and efficiency of matching. By setting multiple matching degree thresholds, the system can retain the most suitable candidates and eliminate unsuitable objects at each step of screening, further improving the quality of recommendations.

[0061] Embodiment 2:

[0062] The above embodiment 1 is a simple introduction to the system provided by the embodiments of the present application. The following other embodiments will further illustrate the system.

[0063] The reference object determination module 202 of the system is introduced, the object information includes historical work information, education information and object evaluation information, and the industry information includes industry description information, industry development trend information and industry evaluation information. Referring to Figure 3 The reference object determination module 202 includes:

[0064] The first industry matching degree determination unit 2021 is configured to determine the first industry matching degree between each initial object and the industry based on the historical work information, the education information of each initial object and the industry description information.

[0065] The second industry matching degree determination unit 2022 is configured to determine the second industry matching degree between each initial object and the industry based on the object evaluation information of each initial object and the industry development trend information.

[0066] The third industry matching degree determination unit 2023 is configured to determine the third industry matching degree between each initial object and the industry based on the education information of each initial object, the object evaluation information and the industry evaluation information.

[0067] The first fusion unit 2024 is configured to fuse the first industry matching degree, the second industry matching degree and the third industry matching degree between each initial object and the industry to obtain the matching degree between each initial object and the industry.

[0068] The first matching unit 2025 is configured to determine the initial object in the plurality of initial objects as a reference object if the matching degree between the initial object and the industry is greater than or equal to the first matching degree threshold, to obtain the plurality of reference objects.

[0069] In a possible implementation, the technical scheme of determining the plurality of reference objects from the plurality of initial objects based on the object information and the industry information of the plurality of initial objects in the above embodiment 2. The object information includes historical work information, education information and object evaluation information, and the industry information includes industry description information, industry development trend information and industry evaluation information.

[0070] In one possible implementation, the reference object is determined in this embodiment 2 through multi-dimensional matching calculation. First, the first matching degree is calculated using the historical work information, education information and industry description information of the object, reflecting the matching of the object's work experience and education background with the industry. Second, the second matching degree is calculated using the object evaluation information and industry development trend information, reflecting the consistency of the object's evaluation with the industry development direction. Third, the third matching degree is calculated using the education information, object evaluation information and industry evaluation information, reflecting the consistency of the object's education background and evaluation with the overall evaluation of the industry. Finally, the three matching degrees are fused to obtain the overall matching degree, and the final reference object is selected based on the threshold.

[0071] As a specific example, assume there is an initial object A, whose historical work information shows that he has worked as a software engineer in multiple internet companies, education information shows that he has a bachelor's degree in computer science, and object evaluation information mentions that he is "good at learning new technologies" and "has good team collaboration skills". The target industry is the artificial intelligence industry, the industry description information mentions "solid programming foundation and machine learning knowledge are required", the industry development trend information points out "deep learning and natural language processing are the focus of future development", and the industry evaluation information emphasizes "innovation ability and fast learning ability are important".

[0072] In calculating the first industry matching degree, the system compares A's software engineer experience and computer science degree with the relevance of the artificial intelligence industry description, and may get a higher matching degree, such as 0.8 (assuming the full score is 1). In calculating the second industry matching degree, the system analyzes the consistency of A's "good at learning new technologies" feature with the industry development trend, and may get a moderate matching degree, such as 0.6. In calculating the third industry matching degree, the system compares the consistency of A's learning ability with the emphasis on fast learning ability in the industry evaluation, and may get a higher matching degree, such as 0.7.

[0073] Finally, the system may use weighted average to fuse the three matching degrees, assuming the weights are 0.4, 0.3 and 0.3 respectively, then the final matching degree is 0.80.4+0.60.3+0.7*0.3=0.71. If the first matching degree threshold is set to 0.7, the initial object A will be determined as the reference object.

[0074] Embodiment 2 can effectively screen out reference objects with high matching degree from multiple initial objects. Compared with traditional methods that rely on a single dimension (such as work experience), the method of the present application can more comprehensively evaluate the suitability of candidates. For example, even if a candidate has no direct artificial intelligence work experience, but if he has strong learning ability and relevant technical background, he may be identified as a potential suitable candidate. This method not only improves the accuracy of talent matching, but also expands the talent pool of enterprises, helping to discover potential outstanding talents.

[0075] In addition, the method provided by Embodiment 2 also has strong flexibility and scalability. By adjusting the weights of different dimensions or adding new matching dimensions, it can easily adapt to the special needs of different industries or enterprises. For example, for emerging industries that are developing rapidly, the weight of innovation ability and adaptability can be increased; for industries that require a lot of team collaboration, the evaluation of communication ability and team spirit can be increased.

[0076] Embodiment 3:

[0077] Further description is made to the first industry matching degree determination unit 2021.

[0078] In one possible implementation, the historical work information includes historical work content, historical work enterprise, and historical work industry, and the education information includes historical course information, historical school information, educational degree information, and skill training information.

[0079] The first industry matching degree determination unit 2021 is configured to determine, for any initial object in the plurality of initial objects, a historical work matching degree between the initial object and the industry based on historical work content, historical work enterprise, historical work industry of the initial object, and the industry description information. Determine the educational and skill matching degree between the initial object and the industry based on the educational degree information, skill training information of the initial object, and the industry description information. Query the historical course information and historical school information of the initial object to obtain the historical course content and historical employment direction corresponding to the initial object. Determine the direction matching degree between the initial object and the industry based on the historical course content, historical employment direction corresponding to the initial object, and the industry description information. Determine the first industry matching degree between the initial object and the industry based on the historical work matching degree, the educational and skill matching degree, and the historical employment direction between the initial object and the industry.

[0080] The above technical solution includes the following main processes:

[0081] In one possible implementation, the processing of historical work information: the historical work information includes historical work content, historical work enterprise, and historical work industry. These information are used to determine the historical work matching degree between the initial object and the industry. The historical work information can be compared with the industry description information through methods such as text analysis and semantic matching, and the matching degree can be obtained.

[0082] In one possible implementation, the processing of education information: the education information includes historical course information, historical school information, educational degree information, and skill training information. These information are used to determine the educational skill matching degree and the direction matching degree. The education information can be mapped with the knowledge and skills required by the industry by establishing an industry knowledge graph, and the matching degree can be obtained.

[0083] In one possible implementation, the analysis of historical course content and historical employment direction: by querying the historical course information and historical school information of the initial object, more detailed historical course content and historical employment direction can be obtained. These information can be analyzed through natural language processing technology to extract keywords and topics, and compared with industry description information to obtain the direction matching degree.

[0084] In one possible implementation, the fusion of multi-dimensional matching degree: the historical work matching degree, the educational skill matching degree, and the direction matching degree in the technical solution of embodiment 3 are fused to obtain the final first industry matching degree. The fusion method can use weighted average, machine learning model, etc. to balance the importance of each dimension.

[0085] It should be noted that there is a close relationship and interaction between these features. For example, the historical work information and the education information may overlap, and appropriate weight distribution is needed to avoid repeated calculation of information. At the same time, the analysis results of historical course content and historical employment direction may affect the historical work matching degree and the educational skill matching degree, and the interaction needs to be considered in the fusion process.

[0086] In embodiment 3, by comprehensively considering the historical work experience, education background and industry information of the initial object, the comprehensive evaluation of the matching degree between the initial object and the specific industry is realized. Specifically, in embodiment 3, the historical work matching degree, the educational skill matching degree and the direction matching degree are calculated respectively, and then these matching degrees are integrated to obtain the final first industry matching degree.

[0087] Compared with traditional simple keyword matching or single-dimensional matching, this multi-dimensional matching calculation method can more comprehensively and accurately evaluate the matching degree of the initial object and the industry. By considering the historical work content, historical work enterprise and historical work industry, it can comprehensively understand whether the work experience of the initial object is related to the target industry. At the same time, by analyzing the education background, including education, skill training, historical course content and historical employment direction, it can evaluate whether the knowledge reserve and career development direction of the initial object match the target industry.

[0088] The technical solutions in embodiment 3 can be implemented as follows in actual application:

[0089] First, for the historical work information of the initial object, natural language processing technology can be used to extract keywords and topics. For example, for a software developer, keywords such as "Java development", "database design" and "agile development" can be extracted. Then, semantic matching is performed between these keywords and industry description information to calculate the matching degree.

[0090] For education information, an industry-subject mapping table can be established to match the degree information and skill training information with industry demand. For example, for the IT industry, computer science, software engineering and other related majors may be given higher matching degrees.

[0091] When analyzing the historical course content and historical employment direction, a text clustering algorithm can be used to classify the course content and employment direction into different topics, and then perform topic matching with industry description information. For example, if a person's course content contains a large number of data analysis related courses, and the target industry is the big data industry, a higher direction matching degree will be obtained.

[0092] Finally, when fusing the matching degrees of each dimension, a weighted average method can be used. For example, the historical work matching degree can be given a weight of 0.4, the education skill matching degree can be given a weight of 0.3, and the direction matching degree can be given a weight of 0.3. These weights can be adjusted according to actual conditions.

[0093] In this way, the technical solutions in embodiment 3 can more comprehensively and accurately evaluate the matching degree of the initial object and the industry, thereby solving the technical problem of determining the first industry matching degree between the initial object and the industry.

[0094] Embodiment 4:

[0095] Further introduction is made to the second industry matching degree determination unit 2022.

[0096] The object evaluation information includes subjective evaluation information and objective evaluation information.

[0097] The second industry matching degree determination unit 2022 is configured to determine, for any initial object in the plurality of initial objects, a subjective matching degree between the initial object and the industry based on subjective evaluation information of the initial object and the industry development trend information, the subjective evaluation information being evaluation information submitted by the initial object. Determine an objective matching degree between the initial object and the industry based on objective evaluation information of the initial object and the industry development trend information, the objective evaluation information being evaluation information submitted by a target object recommendation platform that recommends the initial object. Determine the second industry matching degree between the initial object and the industry based on the subjective matching degree and the objective matching degree between the initial object and the industry.

[0098] In a possible implementation, for any initial object in the plurality of initial objects, a subjective matching degree between the initial object and the industry is determined based on subjective evaluation information of the initial object and industry development trend information. The subjective evaluation information is evaluation information submitted by the initial object. This step can be implemented in various ways, for example, natural language processing techniques can be used to analyze keywords in the subjective evaluation information, and then matched with the industry development trend information. Another way is to establish a scoring model to give scores according to different dimensions in the subjective evaluation information, and then perform weighted calculation with the industry development trend information.

[0099] In a possible implementation, an objective matching degree between the initial object and the industry is determined based on objective evaluation information of the initial object and industry development trend information. The objective evaluation information is evaluation information submitted by a target object recommendation platform that recommends the initial object. This step can use a method similar to the calculation of the subjective matching degree, but needs to consider that the objective evaluation information may contain more structured data, such as scores, rankings, etc. Therefore, a more complex algorithm can be designed to process these data, for example, a machine learning algorithm can be used to train a model that can predict the matching degree according to the objective evaluation information and the industry development trend information.

[0100] In a possible implementation, a second industry matching degree between the initial object and the industry is determined based on the subjective matching degree and the objective matching degree between the initial object and the industry. This step can be implemented in various ways, for example, the average of the subjective matching degree and the objective matching degree can be simply taken. Another more complex method is to assign different weights to the subjective matching degree and the objective matching degree according to the characteristics of different industries, and then perform weighted averaging. More complex fusion algorithms such as support vector machines (SVM) or random forests can also be used to consider the subjective matching degree and the objective matching degree to obtain the final second industry matching degree.

[0101] By this method, the application can more comprehensively and accurately evaluate the matching degree between the initial object and the industry. The subjective evaluation information reflects the initial object's own understanding and expectations of the industry, while the objective evaluation information provides a third-party professional evaluation. Combined with industry development trend information, not only the current matching situation is considered, but also the future development potential is considered. This multi-dimensional evaluation method can greatly improve the accuracy and comprehensiveness of matching.

[0102] The technical solutions in embodiment 4 realize multi-dimensional evaluation of the matching degree between the initial object and the industry by introducing subjective evaluation information and objective evaluation information, and combining with industry development trend information. First, the scheme calculates the subjective matching degree and the objective matching degree respectively, which can comprehensively consider the initial object's own evaluation and the third-party evaluation. Second, the industry development trend information is introduced in the calculation process, so that the matching degree evaluation not only considers the current situation, but also considers the future development. Finally, by synthesizing the subjective and objective matching degrees, the final second industry matching degree is obtained, realizing more accurate and comprehensive matching degree evaluation.

[0103] As a preferred implementation, the following specific steps can be used to realize the above technical solutions:

[0104] 1. Data collection: Collect the subjective evaluation information and the objective evaluation information of the initial object. The subjective evaluation information can be collected through questionnaire survey or online form, including the initial object's evaluation of its own ability, interest, career planning, etc. The objective evaluation information can be obtained from the target object recommendation platform, including the initial object's skill score, work performance evaluation, etc. At the same time, collect industry development trend information, which can be obtained from industry reports, expert forecasts, etc.

[0105] 2. Data preprocessing: Clean and standardize the collected data. For example, convert text data to structured data, and unify the scoring standards from different sources.

[0106] 3. Subjective matching degree calculation: Use natural language processing technology to analyze the subjective evaluation information, extract keywords and topics. Then, use cosine similarity algorithm to calculate the similarity between these keywords and topics and the industry development trend information, and obtain the subjective matching degree score.

[0107] 4. Objective matching degree calculation: Use machine learning algorithms (such as random forest or gradient boosting tree) to train a model, with the input being the objective evaluation information and the industry development trend information, and the output being the objective matching degree score.

[0108] 5. Matching degree fusion: use weighted average method to fuse subjective matching degree and objective matching degree. The weight can be adjusted according to the characteristics of different industries. For example, for industries that require innovation ability, give higher weight to subjective matching degree; for industries that require stability, give higher weight to objective matching degree.

[0109] 6. Result output: output the calculated second industry matching degree as the final result, and set a threshold, above which the initial object is considered to have high industry matching degree.

[0110] Through this implementation, the technical solution in embodiment 4 can make full use of both subjective and objective evaluation information, combined with industry development trend, to obtain a more comprehensive and accurate matching result. This method not only considers the self-evaluation of the initial object, but also considers the professional evaluation of the third party, and also takes the future development of the industry into consideration, thereby greatly improving the accuracy and forward-looking of the matching.

[0111] Embodiment 5:

[0112] Further introduce the third industry matching degree determination unit 2023.

[0113] The education information includes academic degree information and skill training information, and the evaluation information includes subjective evaluation information and objective evaluation information.

[0114] The third industry matching degree determination unit 2023 is configured to determine, for any initial object in the plurality of initial objects, a first evaluation matching degree between the initial object and the industry based on the skill training information of the initial object and the industry evaluation information. Determine the second evaluation matching degree between the initial object and the industry based on the subjective evaluation information, the objective evaluation information of the initial object and the industry evaluation information. Determine the third industry matching degree between the initial object and the industry based on the skill evaluation matching degree and the evaluation matching degree between the initial object and the industry.

[0115] In one possible implementation, for any initial object in the plurality of initial objects, the first evaluation matching degree between the initial object and the industry is determined based on the skill training information of the initial object and the industry evaluation information. This step can be realized in many ways, for example, natural language processing technology can be used to analyze the keywords in the skill training information, and then match with the industry evaluation information. Another way is to establish a scoring model, and give scores according to different dimensions in the skill training information, and then perform weighted calculation with the industry evaluation information.

[0116] In a possible implementation, the second evaluation matching degree between the initial object and the industry is determined based on the subjective evaluation information, the objective evaluation information and the industry evaluation information of the initial object. This step can adopt a method similar to the calculation of the subjective matching degree, but needs to consider that the subjective evaluation information and the objective evaluation information can contain more structured data. Therefore, a more complex algorithm can be designed to process these data, for example, a machine learning algorithm can be used to train a model which can predict the second evaluation matching degree according to the subjective evaluation information, the objective evaluation information and the industry evaluation information.

[0117] In a possible implementation, the third industry matching degree between the initial object and the industry is determined based on the skill evaluation matching degree and the evaluation matching degree between the initial object and the industry. This step can be implemented in various ways, for example, the average of the skill evaluation matching degree and the evaluation matching degree can be simply taken. Another more complex method is that different weights can be assigned to the skill evaluation matching degree and the evaluation matching degree according to the characteristics of different industries, and then a weighted average is taken. A more complex fusion algorithm such as support vector machine (SVM) or random forest can also be used to comprehensively consider the skill evaluation matching degree and the evaluation matching degree to obtain the final second industry matching degree.

[0118] Embodiment 6:

[0119] Further description is made to the first fusion unit 2024.

[0120] In a possible implementation, the first fusion unit 2024 is configured to perform weighted fusion of the first industry matching degree, the second industry matching degree and the third industry matching degree between each initial object and the industry to obtain the matching degree between each initial object and the industry.

[0121] The weights of the weighted fusion are set by a technician according to actual conditions, which are not limited in the embodiments of the present application.

[0122] Embodiment 7:

[0123] Further description is made to the candidate object determination module 203 of the system. The enterprise information includes enterprise culture information, enterprise style information and enterprise planning information, the enterprise evaluation information includes work intensity evaluation information, work content evaluation information and work development evaluation information, and the object information includes historical work information and object evaluation information. See Figure 4 The candidate object determination module 203 comprises:

[0124] The first enterprise matching degree determination unit 2031 is configured to determine a first enterprise matching degree between each reference object and the target enterprise based on the enterprise culture information, the work intensity evaluation information, and the historical work information and the object evaluation information of each reference object.

[0125] The second enterprise matching degree determination unit 2032 is configured to determine a second enterprise matching degree between each reference object and the target enterprise based on the enterprise style information, the work content evaluation information, and the object evaluation information of each reference object.

[0126] The third enterprise matching degree determination unit 2033 is configured to determine a third enterprise matching degree between each reference object and the target enterprise based on the enterprise planning information, the work development evaluation information, and the object evaluation information.

[0127] The second fusion unit 2034 is configured to fuse the first enterprise matching degree, the second enterprise matching degree, and the third enterprise matching degree between each reference object and the target enterprise to obtain a matching degree between each reference object and the target enterprise.

[0128] The second matching unit 2035 is configured to determine, as candidate objects, reference objects in the plurality of reference objects that have a matching degree greater than or equal to the second matching degree threshold with the target enterprise, to obtain the plurality of candidate objects.

[0129] The enterprise information includes enterprise culture information, enterprise style information, and enterprise planning information. These three types of information can describe the characteristics of the enterprise from multiple perspectives. For example, the enterprise culture information can include the values, mission, and vision of the enterprise; the enterprise style information can include the management style, work atmosphere, and team collaboration method; and the enterprise planning information can include the development strategy and future planning of the enterprise. The enterprise evaluation information includes work intensity evaluation information, work content evaluation information, and work development evaluation information. These evaluation information can come from employees, former employees, or third-party evaluation agencies within the enterprise. The work intensity evaluation information can include work duration, stress level, etc.; the work content evaluation information can include the challenge and innovation of the work; and the work development evaluation information can include promotion opportunities and training opportunities. The object information includes historical work information and object evaluation information. The historical work information can include past work experience, position, industry, etc.; and the object evaluation information can include the evaluation of the former employer and the evaluation of the colleagues.

[0130] The technical solution of the present application determines the candidate objects through the following steps:

[0131] First, based on the enterprise culture information, work intensity evaluation information, and historical work information and object evaluation information of each reference object, the first enterprise matching degree between each reference object and the target enterprise is determined. This step mainly considers whether the reference object is compatible with the enterprise culture and whether it can adapt to the work intensity of the enterprise.

[0132] Second, based on the enterprise style information, work content evaluation information, and object evaluation information of each reference object, the second enterprise matching degree between each reference object and the target enterprise is determined. This step mainly considers whether the reference object adapts to the work style of the enterprise and whether it is interested in the work content.

[0133] Third, based on the enterprise planning information, work development evaluation information, and object evaluation information, the third enterprise matching degree between each reference object and the target enterprise is determined. This step mainly considers whether the career development direction of the reference object is consistent with the planning of the enterprise.

[0134] Then, the first enterprise matching degree, the second enterprise matching degree, and the third enterprise matching degree between each reference object and the enterprise are fused to obtain the matching degree between each reference object and the target enterprise. The fusion method can use weighted average, geometric mean, or other more complex algorithms to ensure that the matching degree of each dimension is reasonably considered.

[0135] Finally, the reference objects with a matching degree greater than or equal to the second matching degree threshold between the enterprise and the multiple reference objects are determined as candidate objects to obtain multiple candidate objects. The second matching degree threshold can be adjusted according to actual needs to control the number and quality of candidate objects.

[0136] This multi-dimensional matching method can more comprehensively evaluate the matching degree between the reference object and the target enterprise. For example, a reference object may be very matched with the enterprise in terms of work content, but there is a large difference in enterprise culture. By considering multiple dimensions comprehensively, a more balanced and comprehensive matching result can be obtained.

[0137] In addition, the technical solution of the present application also has flexibility and scalability. The weights of each dimension can be adjusted or new matching dimensions can be added according to the characteristics of different industries or enterprises. For example, for innovative enterprises, more emphasis may be placed on the innovation ability and learning ability of reference objects; for service-oriented enterprises, more emphasis may be placed on the communication ability and service consciousness of reference objects.

[0138] As a preferred implementation, a machine learning algorithm can be employed to optimize the matching process. By analyzing historical successful matching cases, the system can continuously learn and adjust the weights and matching rules of various dimensions, thereby improving the accuracy of matching. For example, a support vector machine (SVM) or random forest algorithm can be used to train the matching model, with input features including enterprise information, enterprise evaluation information, and object information of reference objects, and output being a matching degree score.

[0139] In practical applications, the technical solutions of the present application can be seamlessly integrated with other recruitment processes. For example, after preliminary screening of candidate objects, further information such as interview evaluation and skill test can be combined to conduct a more in-depth assessment of the candidate objects. This multi-stage, multi-dimensional evaluation method can greatly improve the accuracy and efficiency of talent matching.

[0140] The technical solution provided in Embodiment 7 further proposes a technical solution for determining a plurality of candidate objects from a plurality of reference objects based on enterprise information of a target enterprise, enterprise evaluation information, and object information of the plurality of reference objects.

[0141] The technical solution provided in Embodiment 7 assesses the matching degree of reference objects and target enterprises from multiple dimensions and levels, including enterprise culture, work intensity, enterprise style, work content, enterprise planning, and work development. This method not only considers objective information of the enterprise, but also combines evaluation information, and fully utilizes historical work information and evaluation information of the objects, thereby being able to more accurately identify candidate objects with high matching degree to the target enterprise.

[0142] Compared with traditional simple keyword matching or single-dimensional screening, this multi-dimensional matching method can more comprehensively and deeply assess the matching degree of reference objects and target enterprises, effectively improving the accuracy and efficiency of matching. At the same time, by setting a matching degree threshold, the number and quality of candidate objects can be flexibly controlled, laying a foundation for subsequent precise recommendation.

[0143] Embodiment 8:

[0144] Further description is made to the first enterprise matching degree determination unit 2031.

[0145] The first enterprise matching degree determination unit 2031 is configured to determine, for any reference object in the plurality of reference objects, an enterprise culture matching degree between the reference object and the target enterprise based on the enterprise culture information and the object evaluation information of the reference object. Determine the work intensity matching degree between the reference object and the target enterprise based on the work intensity evaluation information and the historical work information of the reference object. Determine the first enterprise matching degree between the reference object and the target enterprise based on the enterprise culture matching degree and the work intensity matching degree between the reference object and the target enterprise.

[0146] The determination of the enterprise culture matching degree can be realized in various ways. For example, natural language processing technology can be used to analyze the enterprise culture information and the object evaluation information of the reference object, extract keywords and calculate semantic similarity. Another method is to establish enterprise culture feature vectors and reference object feature vectors, and evaluate the matching degree by calculating the vector distance.

[0147] The determination of the work intensity matching degree can also be realized in various ways. The specific indicators in the work intensity evaluation information, such as working hours and task complexity, can be analyzed and compared with the historical work information of the reference object. Another method is to use a machine learning model to train a prediction model based on historical data to evaluate whether the reference object can adapt to the work intensity of the target enterprise.

[0148] The combination of enterprise culture matching degree and work intensity matching degree can be realized by weighted average, multi-objective optimization or machine learning model. This combination not only considers the importance of the two factors, but also adjusts the weight of each factor according to the needs of different enterprises.

[0149] The technical solution provided by embodiment 8 solves the problem of how to determine the first enterprise matching degree between the reference object and the target enterprise. First, by analyzing the enterprise culture information and the object evaluation information of the reference object, the enterprise culture matching degree is determined. This step can effectively evaluate whether the reference object is suitable for the cultural environment of the target enterprise, thereby predicting its adaptability and potential performance in the enterprise.

[0150] Then, the technical solution provided by embodiment 8 determines the work intensity matching degree using the work intensity evaluation information and the historical work information of the reference object. This step can evaluate whether the reference object can adapt to the work rhythm and pressure of the target enterprise, thereby predicting its performance in actual work.

[0151] Finally, the technical solution provided by Embodiment 8 combines the enterprise culture matching degree and the work intensity matching degree to obtain the first enterprise matching degree. This comprehensive evaluation method not only considers the skills and experience of the reference object, but also considers its adaptability in terms of enterprise culture and work intensity, thereby providing a more comprehensive and accurate matching result.

[0152] Through this multi-dimensional evaluation method, the present application can more accurately identify candidates suitable for the target enterprise, improve recruitment efficiency, reduce the risk of talent loss, and at the same time, can find a more suitable work environment for job seekers and improve employment satisfaction.

[0153] Embodiment 8 further proposes that for any reference object in the plurality of reference objects, based on the enterprise culture information and the object evaluation information of the reference object, the enterprise culture matching degree between the reference object and the target enterprise is determined; based on the work intensity evaluation information and the historical work information of the reference object, the work intensity matching degree between the reference object and the target enterprise is determined; and based on the enterprise culture matching degree and the work intensity matching degree between the reference object and the target enterprise, the first enterprise matching degree between the reference object and the target enterprise is determined.

[0154] The technical solution provided by Embodiment 8 improves the accuracy and comprehensiveness of the matching by considering the two important factors of enterprise culture and work intensity. It not only focuses on skill matching, but also considers cultural adaptability and work intensity adaptability, which is very important for ensuring long-term employment relationships and employee satisfaction.

[0155] The innovative point of the technical solution provided by Embodiment 8 is to combine qualitative enterprise culture information and quantitative work intensity evaluation to form a comprehensive matching degree evaluation system. This method is more comprehensive and effective than traditional matching methods based only on skills and experience, and can better predict the performance and adaptability of job seekers in a specific enterprise environment.

[0156] As a preferred implementation, the technical solution provided by Embodiment 8 can be implemented through the following steps:

[0157] 1. Enterprise culture matching degree calculation: use natural language processing technology to analyze enterprise culture information and extract keywords such as "innovation", "teamwork", "customer orientation", etc. Similarly, analyze the object evaluation information of the reference object and extract relevant keywords. Calculate the cosine similarity of the two sets of keywords to obtain a score between 0 and 1, representing the enterprise culture matching degree.

[0158] 2. Work intensity matching degree calculation: analyze the work intensity evaluation information, extract specific indicators such as average working hours, overtime frequency, task complexity, etc. At the same time, analyze the historical work information of the reference object and extract the corresponding indicators. Normalize each indicator, then calculate the Euclidean distance to get a score between 0 and 1, representing the work intensity matching degree.

[0159] 3. First enterprise matching degree calculation: Set weights w1 and w2 (w1 + w2 = 1) to represent the importance of enterprise culture matching degree and work intensity matching degree. Use weighted average method to calculate the final first enterprise matching degree: First enterprise matching degree = w1 * enterprise culture matching degree + w2 * work intensity matching degree

[0160] In the above manner, a comprehensive matching score can be generated for each reference object, thereby helping the target enterprise to better screen suitable candidates.

[0161] Embodiment 9:

[0162] Further introduce the second enterprise matching degree determination unit 2032.

[0163] The object evaluation information includes subjective evaluation information and objective evaluation information.

[0164] The second enterprise matching degree determination unit 2032 is configured to determine, for any reference object in the plurality of reference objects, an enterprise style matching degree between the reference object and the target enterprise based on the enterprise style information and objective evaluation information of the reference object, the objective evaluation information being evaluation information submitted by a target object recommendation platform that recommends the initial object. Determine the work content matching degree between the reference object and the target enterprise based on the work content evaluation information and the subjective evaluation information of the reference object, the subjective evaluation information being evaluation information submitted by the initial object. The enterprise style matching degree and the work content matching degree between the reference object and the target enterprise determine the second enterprise matching degree between the reference object and the target enterprise.

[0165] Wherein, for any one of the plurality of reference objects, the enterprise style matching degree between the reference object and the target enterprise is determined based on the enterprise style information and the objective evaluation information of the reference object. The objective evaluation information is submitted by the target object recommendation platform of the recommendation initial object, which can reduce subjective bias and improve the objectivity of the matching. The work content matching degree between the reference object and the target enterprise is determined based on the work content evaluation information and the subjective evaluation information of the reference object. The subjective evaluation information is submitted by the initial object, which can fully consider the individual's feelings and preferences for the work content and improve the individualization degree of the matching. The enterprise style matching degree and the work content matching degree between the reference object and the target enterprise are comprehensively considered to determine the second enterprise matching degree between the reference object and the target enterprise. This comprehensive consideration can balance the objective factors and subjective factors to obtain a more comprehensive and accurate matching result.

[0166] Specifically, the calculation of the enterprise style matching degree can adopt a multi-dimensional evaluation method. For example, the enterprise style information can be decomposed into enterprise culture, management style, work environment, etc. multiple dimensions, and then each dimension is evaluated separately. For each dimension, a series of evaluation indicators can be set, and the corresponding scores can be given according to the objective evaluation information. Finally, the overall enterprise style matching degree is obtained by weighted average or other comprehensive calculation method.

[0167] The calculation of the work content matching degree can adopt the method of semantic analysis and similarity calculation. First, the work content evaluation information and the subjective evaluation information of the reference object are subjected to semantic analysis to extract keywords and topics. Then, using the vector space model or other similarity calculation method, the similarity between the two is calculated. In addition, machine learning algorithms can also be introduced to continuously optimize the matching model through training data.

[0168] In determining the second enterprise matching degree, a weighted average method can be used. For example, the weight of the enterprise style matching degree can be set to 0.6, and the weight of the work content matching degree can be set to 0.4. By adjusting these weights, the influence of objective factors and subjective factors in the final matching result can be flexibly balanced.

[0169] As a preferred embodiment, the technical solution provided by embodiment 9 can also introduce a time decay factor to better reflect the timeliness of the evaluation information. For example, older evaluation information can be given a smaller weight, while newer evaluation information can be given a larger weight. This can ensure that the matching result is closer to the current situation of the reference object and the target enterprise.

[0170] In addition, the technical scheme provided by embodiment 9 can also filter the matching results by setting a threshold. For example, a minimum matching degree threshold can be set, and only when the matching degree of the second enterprise exceeds the threshold, the reference object is included in the candidate object pool. In this way, the quality of the recommendation can be effectively improved, and irrelevant matching results can be reduced.

[0171] The technical scheme improves the accuracy and comprehensiveness of matching by dividing the object evaluation information into subjective evaluation information and objective evaluation information, and using them for different aspects of matching degree calculation. The scheme first determines the enterprise style matching degree based on enterprise style information and objective evaluation information, then determines the work content matching degree based on work content evaluation information and subjective evaluation information, and finally obtains the second enterprise matching degree by combining the two aspects. This multi-dimensional and multi-level matching method can more comprehensively evaluate the matching degree between the reference object and the target enterprise, effectively solving the technical problem of how to more accurately determine the second enterprise matching degree between the reference object and the target enterprise.

[0172] Embodiment 10:

[0173] Further description is made to the third enterprise matching degree determination unit 2033.

[0174] The object evaluation information includes subjective evaluation information and objective evaluation information. The third enterprise matching degree determination unit 2033 is configured to determine, for any reference object in the plurality of reference objects, an enterprise planning matching degree between the reference object and the target enterprise based on the enterprise planning information and the objective evaluation information of the reference object. Determine the work development matching degree between the reference object and the target enterprise based on the work development evaluation information and the subjective evaluation information of the reference object. Determine the third enterprise matching degree between the reference object and the target enterprise based on the enterprise planning matching degree and the work development matching degree between the reference object and the target enterprise.

[0175] In a possible implementation, for any reference object in the plurality of reference objects, the enterprise planning matching degree between the reference object and the target enterprise is determined based on the enterprise planning information and the objective evaluation information of the reference object. The calculation of the enterprise planning matching degree can use the method of semantic analysis and similarity calculation. First, perform semantic analysis on the enterprise planning information and the objective evaluation information of the reference object to extract keywords and topics. Then, use the vector space model or other similarity calculation method to calculate the similarity between the two. In addition, machine learning algorithms can also be introduced to continuously optimize the matching model through training data.

[0176] In a possible implementation, based on the work development evaluation information and the subjective evaluation information of the reference object, the work development matching degree between the reference object and the target enterprise is determined. The work development matching degree can be calculated by using semantic analysis and similarity calculation. First, the work development evaluation information and the subjective evaluation information of the reference object are subjected to semantic analysis to extract keywords and topics. Then, the similarity between the two is calculated by using a vector space model or other similarity calculation methods. In addition, a machine learning algorithm can also be introduced to continuously optimize the matching model through training data.

[0177] In a possible implementation, based on the enterprise planning matching degree and the work development matching degree between the reference object and the target enterprise, a third enterprise matching degree between the reference object and the target enterprise is determined. In determining the third enterprise matching degree, a weighted average method can be used. For example, the weight of the enterprise planning matching degree can be set to 0.6, and the weight of the work development matching degree can be set to 0.4. By adjusting these weights, the influence of various factors on the final matching result can be flexibly adjusted.

[0178] Embodiment 11:

[0179] The second fusion unit 2034 will be further described.

[0180] In a possible implementation, the second fusion unit 2034 is configured to fuse the first enterprise matching degree, the second enterprise matching degree, and the third enterprise matching degree between each reference object and the target enterprise by weighting, to obtain a matching degree between each reference object and the target enterprise.

[0181] The weights of the weighted fusion are set by technicians according to actual conditions, and embodiments of the present application are not limited in this regard.

[0182] Embodiment 12:

[0183] The target object determination module 204 of the system will be described. The job information includes job requirement information, job description information, and job development information. The object information includes historical work information, education information, and object evaluation information. Referring to Figure 5 The target object determination module 204 includes:

[0184] The first job matching degree determination unit 2041 is configured to determine a first job matching degree between each candidate object and the target job based on the historical work information, the education information of each candidate object, and the job requirement information.

[0185] The second position matching degree determination unit 2042 is configured to determine a second position matching degree between each candidate object and the target position based on the historical work information of each candidate object and the position description information.

[0186] The third position matching degree determination unit 2043 is configured to determine a third position matching degree between each candidate object and the target position based on the object evaluation information of each candidate object and the position development information.

[0187] The third fusion unit 2044 is configured to fuse the first position matching degree, the second position matching degree and the third position matching degree between each candidate object and the target position to obtain a matching degree between each candidate object and the target position.

[0188] The third matching unit 2045 is configured to determine, as a target object, a candidate object in the plurality of candidate objects that has a matching degree with the target position greater than or equal to the third matching degree threshold, to obtain the at least one target object.

[0189] As a preferred implementation, the technical solution provided in Embodiment 12 can be implemented through the following specific steps:

[0190] 1. Obtain position information of a target position, including position requirement information, position description information and position development information. For example, for a software engineer position, the position requirement information can include “bachelor’s degree or above, 3 years or more of relevant work experience, proficient in Java programming”; the position description information can include “responsible for the development and maintenance of the company’s core business system”; and the position development information can include “have the opportunity to participate in large projects and be promoted to technical experts or management positions”.

[0191] 2. Obtain object information of a plurality of candidate objects, including historical work information, education information and object evaluation information. For example, for candidate A, the historical work information can be “5 years of Java development experience, having participated in the development of an e-commerce platform”; the education information can be “Bachelor of Science in Computer Science”; and the object evaluation information can include “strong technical ability, good at team cooperation”.

[0192] 3. Calculate the first position matching degree:

[0193] – Compare the education of candidate A (Bachelor of Science in Computer Science) with the position requirement (bachelor’s degree or above) to determine the education match.

[0194] – Compare the work experience (5 years) with the requirement (3 years or more) to determine the experience match.

[0195] – Compare the skills (Java development experience) with the requirement (proficient in Java programming) to determine the skill match.

[0196] - Summarize the above three points to get the first position matching degree, for example, 90%.

[0197] 4. Calculate the second position matching degree:

[0198] - Compare the candidate A's historical work content (e-commerce platform development) with the position description (core business system development and maintenance), and evaluate the relevance.

[0199] - Consider the matching degree of the work field.

[0200] - Comprehensive evaluation, get the second position matching degree, for example, 85%.

[0201] 5. Calculate the third position matching degree:

[0202] - Analyze the matching degree of the candidate A's object evaluation information (strong technical ability, good team cooperation) and the position development information.

[0203] - Evaluate whether the candidate's potential meets the promotion path.

[0204] - Comprehensive evaluation, get the third position matching degree, for example, 80%.

[0205] 6. Fusion of three matching degrees:

[0206] - Weighted average method can be used, for example, the weights of the first, second and third position matching degrees are 0.4, 0.3 and 0.3 respectively.

[0207] - Calculate the final matching degree: 0.4*90%+0.3*85%+0.3*80%=85.5%

[0208] 7. Set the matching threshold, for example, 80%. If the final matching degree of candidate A is 85.5% higher than the threshold, it will be determined as the target object.

[0209] 8. Repeat the above steps for all candidate objects, and finally get all the target objects with matching degree higher than the threshold.

[0210] The technical solution provided by embodiment 12 realizes comprehensive evaluation of the matching degree between the candidate object and the target position through multi-dimensional and multi-level matching analysis. Specifically, the position information includes position requirement information, position description information and position development information, which are used to comprehensively describe the target position. The object information includes historical work information, education information and object evaluation information, which are used to comprehensively describe the candidate object.

[0211] The technical solution provided by embodiment 12 first matches the historical work information, education information and position requirement information of the candidate object, evaluates whether the candidate object meets the basic requirements of the position, and obtains a first position matching degree. Secondly, the historical work information of the candidate object is matched with the position description information, and it is evaluated whether the work experience of the candidate object is consistent with the position description, and a second position matching degree is obtained. Thirdly, the evaluation information of the candidate object is matched with the position development information, and it is evaluated whether the potential of the candidate object meets the development needs of the position, and a third position matching degree is obtained. Finally, the matching degrees of the three dimensions are fused to obtain an overall matching degree, and the final target object is screened out by setting a threshold.

[0212] Through this multi-dimensional and multi-level matching analysis method, the technical solution provided by embodiment 12 not only considers whether the candidate object meets the basic requirements of the position, but also evaluates the relevance of the work content and the potential of the future development, so that the candidate object highly matched with the target position can be more comprehensively and accurately identified. Compared with the traditional simple keyword matching or single-dimensional screening, this method can more effectively solve the technical problem of determining the target object with high matching degree with the target position from multiple candidate objects.

[0213] Embodiment 13:

[0214] The first position matching degree determination unit 2041 will be further introduced.

[0215] The historical work information includes historical work content and historical position.

[0216] The first position matching degree determination unit 2041 is configured to determine, for any candidate object in the plurality of candidate objects, a content requirement matching degree between the candidate object and the target position based on the historical work content of the candidate object and the position requirement information. Determine the position matching degree between the candidate object and the target position based on the historical position of the candidate object and the position requirement information. Determine the first position matching degree between the candidate object and the target position based on the content requirement matching degree and the position matching degree between the candidate object and the target position.

[0217] Among them, the content requirement matching degree is determined by comparing the historical work content of the candidate object and the position requirement information. This step can be realized in many ways, for example, using natural language processing technology to perform semantic analysis on the historical work content and the position requirement, and calculating the similarity score. Another method is to extract keywords and perform weighted matching, and give a score according to the matching degree.

[0218] In determining the position matching degree, position hierarchy comparison or position similarity analysis can be used. Position hierarchy comparison can establish a position hierarchy system to compare the differences in hierarchy between the candidate's historical positions and the target position. Position similarity analysis can use machine learning models to train the similarity relationship between different positions based on a large amount of historical data.

[0219] The results of content requirement matching degree and position matching degree can be combined to obtain a first position matching degree through weighted averaging or more complex fusion algorithms. The weights can be adjusted according to the characteristics of different industries or positions to meet the needs of different scenarios.

[0220] This multi-dimensional matching method is more comprehensive and accurate than simple keyword matching. By considering both the work content and the position, the matching degree between the candidate and the target position can be more accurately evaluated. For example, a candidate may not have the exact same position name, but their work content is highly relevant to the target position. In this case, the method can still identify their potential match.

[0221] In specific implementation, a threshold value, such as 0.7 (full score is 1), can be set. When the calculated first position matching degree exceeds this threshold value, it is considered that the candidate has a high degree of match with the target position. The system can further sort the candidates according to this matching degree to provide more accurate talent recommendations for enterprises.

[0222] In practical applications, machine learning techniques can be combined to continuously optimize the matching algorithm. By collecting data from successful recruitment cases, the system can learn which factors are more important for successful matching, and dynamically adjust the weights of each dimension to improve the accuracy of matching.

[0223] Compared with the prior art, the technical solution provided by embodiment 13 has the following obvious advantages: first, it considers both the work content and the position, rather than relying solely on the matching of position names, which greatly improves the accuracy and comprehensiveness of the matching. Second, by introducing historical work information and education information, the solution can more comprehensively evaluate the candidate's background and ability, rather than relying solely on keywords in the resume. Finally, the flexibility of this solution allows it to adapt to the special needs of different industries and positions, optimizing the matching results by adjusting weights and matching algorithms.

[0224] Through this method, the technical solution provided by embodiment 13 can better solve the technical problem of determining the first position matching degree between the candidate and the target position, improve the accuracy of talent matching, and provide more valuable recommendation services for enterprises and job seekers.

[0225] Embodiment 14:

[0226] Further description is made to the second position matching degree determination unit 2042.

[0227] The historical work information includes historical work content and historical work industry.

[0228] The second position matching degree determination unit 2042 is configured to determine, for any candidate object in the plurality of candidate objects, a content description matching degree between the candidate object and the target position based on historical work content of the candidate object and the position description information, determine an industry description matching degree between the candidate object and the target position based on historical work industry of the candidate object and the position description information, and determine a second position matching degree between the candidate object and the target position based on the content description matching degree and the industry description matching degree between the candidate object and the target position.

[0229] The above-described manner of determining the second position matching degree is described below.

[0230] First, the content description matching degree is determined based on the historical work content of the candidate object and the position description information. Natural language processing techniques such as semantic similarity analysis can be used to compare the historical work content with the position description. For example, a word vector model can be used to convert text into vectors, and then the cosine similarity between vectors is calculated. The higher the similarity, the higher the content description matching degree.

[0231] Second, the industry description matching degree is determined based on the historical work industry of the candidate object and the position description information. An industry classification system can be established to match the historical work industry and the industry to which the target position belongs. If they belong to the same industry or related industries, a higher matching score is given. The correlation between industries can also be considered, for example, the IT industry and the electronics industry may have a higher correlation.

[0232] Finally, the content description matching degree and the industry description matching degree are weighted and fused to obtain the second position matching degree. The weights of the two can be adjusted according to actual needs, for example, the weight of the content description matching degree can be set to 0.6 and the weight of the industry description matching degree can be set to 0.4.

[0233] The technical solution provided by embodiment 14 evaluates the matching degree between the candidate object and the target position from multiple dimensions to improve the accuracy and comprehensiveness of the matching. Specifically, the technical solution provided by embodiment 14 evaluates the matching degree from aspects such as content description matching, industry description matching, subjective position development matching, and objective position development matching. By comprehensively considering these factors, the technical solution provided by embodiment 14 can more comprehensively and accurately evaluate the matching degree between the candidate object and the target position, thereby improving the accuracy and efficiency of recruitment recommendation. This multi-dimensional matching method effectively solves the limitations of traditional simple keyword matching or single-dimensional screening, and can better explore the potential of job seekers and the needs of positions, improving the quality of person-job matching.

[0234] Embodiment 15:

[0235] Further description is made to the third position matching degree determination unit 2043.

[0236] The object evaluation information includes subjective evaluation information and objective evaluation information. The third position matching degree determination unit 2043 is configured to, for any candidate object in the plurality of candidate objects, determine a subjective position development matching degree between the candidate object and the target position based on the subjective evaluation information of the candidate object and the position development information, the subjective evaluation information being the evaluation information submitted by the initial object. Determine an objective position development matching degree between the candidate object and the target position based on the objective evaluation information of the candidate object and the position development information, the objective evaluation information being the evaluation information submitted by the target object recommendation platform that recommended the initial object. Determine a third position matching degree between the candidate object and the target position based on the subjective position development matching degree and the objective position development matching degree between the candidate object and the target position.

[0237] The above-mentioned manner of determining the third position matching degree is described below.

[0238] In one possible implementation, the subjective position development matching degree is determined based on the subjective evaluation information of the candidate object and the position development information. The subjective evaluation information can include the career planning, expected salary, work environment preference, etc. of the candidate object. These information can be compared with the position development information, for example, if the career planning of the candidate object is consistent with the development prospects of the position, a higher matching degree score is given.

[0239] Secondly, the objective position development matching degree is determined based on the objective evaluation information of the candidate object and the position development information. The objective evaluation information can include the ability assessment, performance evaluation, etc. of the candidate object by the recommendation platform. These evaluations can be matched with the requirements and development prospects of the position, for example, if the ability assessment result of the candidate object is highly consistent with the requirements of the position, a higher matching degree score is given.

[0240] Finally, the subjective position development matching degree and the objective position development matching degree are weighted and fused to obtain a third position matching degree. The weights of the two can be adjusted according to actual conditions, for example, the weight of the subjective matching degree can be set to 0.4 and the weight of the objective matching degree can be set to 0.6 to balance the influence of personal expectations and objective evaluation.

[0241] In this way, the technical solution provided by embodiment 15 can comprehensively consider the historical work experience, industry background, personal expectations and objective evaluation of the candidate object, so as to more accurately evaluate the matching degree of the candidate object and the target position. This multi-dimensional matching method not only considers the surface work content matching, but also deeply considers the industry matching, personal development matching and other factors, which can better predict the potential performance and development possibility of the candidate object in the target position.

[0242] Embodiment 16:

[0243] Further description is made to the third fusion unit 2044.

[0244] In one possible implementation, the third fusion unit 2044 is configured to weight and fuse the first position matching degree, the second position matching degree and the third position matching degree between each candidate object and the target position to obtain the matching degree between each candidate object and the target position.

[0245] The weights of the weighted fusion are set by technicians according to actual conditions, which are not limited by the embodiments of the present application.

[0246] Embodiment 17:

[0247] The way of determining the target object recommendation platform is described below.

[0248] In one possible implementation, the system further includes a platform determination module configured to obtain platform information and platform evaluation information of a plurality of candidate object recommendation platforms. Based on the platform information and the platform evaluation information of each candidate object recommendation platform, the credibility of each candidate object recommendation platform is determined. The candidate object recommendation platforms with a credibility greater than or equal to a credibility threshold in the plurality of candidate object recommendation platforms are determined as target object recommendation platforms to obtain the plurality of target object recommendation platforms.

[0249] The platform information is provided by each candidate object recommendation platform, and the platform evaluation information is provided by other object recommendation platforms in the plurality of candidate object recommendation platforms, such as, for the first candidate object recommendation platform in the plurality of candidate object recommendation platforms, the platform evaluation information of the first candidate object recommendation platform is provided by other candidate object recommendation platforms in the plurality of candidate object recommendation platforms. The credibility threshold is set by a technician according to actual conditions, and embodiments of the present application are not limited thereto.

[0250] All the optional technical solutions described above can be combined to form optional embodiments of the present application, and will not be repeated here.

[0251] The above are only optional embodiments of the present application, and are not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A big data-based intelligent recruitment and employment recommendation system, characterized in that, The system comprises: An acquisition module is configured to acquire object recruitment information of a target enterprise, object information of a plurality of initial objects, industry information of an industry where the target enterprise is located, and enterprise evaluation information of the target enterprise in response to an object recommendation request of the target enterprise, the plurality of initial objects being objects recommended by a plurality of target object recommendation platforms, the plurality of target object recommendation platforms being object recommendation platforms with a credibility greater than or equal to a credibility threshold, the object recruitment information comprising enterprise information of the target enterprise and position information of a target position, the target position being a position to be recommended, the enterprise information comprising enterprise culture information, enterprise style information, and enterprise planning information, the enterprise evaluation information comprising work intensity evaluation information, work content evaluation information, and work development evaluation information, and the object information comprising historical work information and object evaluation information; A reference object determination module is configured to determine a plurality of reference objects from the plurality of initial objects based on the object information of the plurality of initial objects and the industry information, the reference object being an initial object with a matching degree with the industry where the target enterprise is located greater than or equal to a first matching degree threshold; A candidate object determination module comprises: A first enterprise matching degree determination unit is configured to determine a first enterprise matching degree between each of the reference objects and the target enterprise based on the enterprise culture information, the work intensity evaluation information, and the historical work information and the object evaluation information of each of the reference objects, the first enterprise matching degree being determined based on an enterprise culture matching degree and a work intensity matching degree between each of the reference objects and the target enterprise; A second enterprise matching degree determination unit is configured to determine a second enterprise matching degree between each of the reference objects and the target enterprise based on the enterprise style information, the work content evaluation information, and the object evaluation information of each of the reference objects, the second enterprise matching degree being determined based on an enterprise style matching degree and a work content matching degree between each of the reference objects and the target enterprise; A third enterprise matching degree determination unit is configured to determine a third enterprise matching degree between each of the reference objects and the target enterprise based on the enterprise planning information, the work development evaluation information, and the object evaluation information, the third enterprise matching degree being determined based on an enterprise planning matching degree and a work development matching degree between each of the reference objects and the target enterprise; A second fusion unit is configured to fuse the first enterprise matching degree, the second enterprise matching degree, and the third enterprise matching degree between each of the reference objects and the target enterprise to obtain a matching degree between each of the reference objects and the target enterprise; A second matching unit is configured to determine, as candidate objects, reference objects in the plurality of reference objects with a matching degree with the target enterprise greater than or equal to a second matching degree threshold to obtain a plurality of candidate objects. The target object determination module is configured to determine at least one target object from the plurality of candidate objects based on the position information of the target position and the object information of the plurality of candidate objects, the target object being a reference object having a matching degree with the target position greater than or equal to a third matching degree threshold; The recommendation module is configured to recommend the at least one target object to the target enterprise.

2. The system of claim 1, wherein, The object information includes historical work information, education information, and object evaluation information, and the industry information includes industry description information, industry development trend information, and industry evaluation information; The reference object determination module includes: The first industry matching degree determination unit is configured to determine a first industry matching degree between each of the initial objects and the industry based on the historical work information, the education information of each of the initial objects, and the industry description information; The second industry matching degree determination unit is configured to determine a second industry matching degree between each of the initial objects and the industry based on the object evaluation information of each of the initial objects and the industry development trend information; The third industry matching degree determination unit is configured to determine a third industry matching degree between each of the initial objects and the industry based on the education information, the object evaluation information of each of the initial objects, and the industry evaluation information; The first fusion unit is configured to fuse the first industry matching degree, the second industry matching degree, and the third industry matching degree between each of the initial objects and the industry to obtain a matching degree between each of the initial objects and the industry; The first matching unit is configured to determine, as a reference object, an initial object in the plurality of initial objects having a matching degree with the industry greater than or equal to the first matching degree threshold, to obtain the plurality of reference objects.

3. The system of claim 2, wherein, The historical work information includes historical work content, a historical work enterprise, and a historical work industry, and the education information includes historical course information, historical school information, educational background and degree information, and skill training information; The first industry matching degree determination unit is configured to, for any initial object in the plurality of initial objects, determine a historical work matching degree between the initial object and the industry based on the historical work content, the historical work enterprise, the historical work industry of the initial object, and the industry description information; determine an educational background and skill matching degree between the initial object and the industry based on the educational background and degree information, the skill training information of the initial object, and the industry description information; query the historical course information and the historical school information of the initial object to obtain historical course content and a historical employment direction corresponding to the initial object, and determine a direction matching degree between the initial object and the industry based on the historical course content, the historical employment direction corresponding to the initial object, and the industry description information; determine the first industry matching degree between the initial object and the industry based on the historical work matching degree, the educational background and skill matching degree, and the historical employment direction between the initial object and the industry.

4. The system of claim 2, wherein, The object evaluation information includes subjective evaluation information and objective evaluation information; The second industry matching degree determination unit is configured to determine, for any initial object in the plurality of initial objects, a subjective matching degree between the initial object and the industry based on subjective evaluation information of the initial object and the industry development trend information, the subjective evaluation information being evaluation information submitted by the initial object; determine an objective matching degree between the initial object and the industry based on objective evaluation information of the initial object and the industry development trend information, the objective evaluation information being evaluation information submitted by a target object recommendation platform that recommends the initial object; determine a second industry matching degree between the initial object and the industry based on the subjective matching degree and the objective matching degree between the initial object and the industry.

5. The system of claim 1, wherein, The first enterprise matching degree determination unit is configured to determine, for any reference object in the plurality of reference objects, an enterprise culture matching degree between the reference object and the target enterprise based on the enterprise culture information and object evaluation information of the reference object; determine a work intensity matching degree between the reference object and the target enterprise based on the work intensity evaluation information and historical work information of the reference object; determine a first enterprise matching degree between the reference object and the target enterprise based on the enterprise culture matching degree and the work intensity matching degree between the reference object and the target enterprise.

6. The system of claim 1, wherein, The object evaluation information includes subjective evaluation information and objective evaluation information; The second enterprise matching degree determination unit is configured to determine, for any reference object in the plurality of reference objects, an enterprise style matching degree between the reference object and the target enterprise based on the enterprise style information and objective evaluation information of the reference object, the objective evaluation information being evaluation information submitted by a target object recommendation platform that recommends the initial object; determine a work content matching degree between the reference object and the target enterprise based on the work content evaluation information and subjective evaluation information of the reference object, the subjective evaluation information being evaluation information submitted by the initial object; determine a second enterprise matching degree between the reference object and the target enterprise based on the enterprise style matching degree and the work content matching degree between the reference object and the target enterprise.

7. The system of claim 1, wherein, The position information includes position requirement information, position description information, and position development information, and the object information includes historical work information, education information, and object evaluation information; The target object determination module includes: A first position matching degree determination unit configured to determine a first position matching degree between each candidate object and the target position based on historical work information, education information of each candidate object, and the position requirement information; A second position matching degree determination unit configured to determine a second position matching degree between each candidate object and the target position based on historical work information of each candidate object and the position description information; The third position matching degree determination unit is configured to determine a third position matching degree between each of the candidate objects and the target position based on the object evaluation information of each of the candidate objects and the position development information. The third fusion unit is configured to fuse the first position matching degree, the second position matching degree and the third position matching degree between each of the candidate objects and the target position to obtain a matching degree between each of the candidate objects and the target position. The third matching unit is configured to determine, as a target object, a candidate object in the plurality of candidate objects that has a matching degree with the target position greater than or equal to the third matching degree threshold, to obtain the at least one target object.

8. The system of claim 7, wherein, The historical work information includes historical work content and historical position; The first position matching degree determination unit is configured to, for any candidate object in the plurality of candidate objects, determine a content requirement matching degree between the candidate object and the target position based on the historical work content of the candidate object and the position requirement information. determine a position matching degree between the candidate object and the target position based on the historical position of the candidate object and the position requirement information. determine a first position matching degree between the candidate object and the target position based on the content requirement matching degree and the position matching degree between the candidate object and the target position.

9. The system of claim 7, wherein, The historical work information includes historical work content and historical work industry; The second position matching degree determination unit is configured to, for any candidate object in the plurality of candidate objects, determine a content description matching degree between the candidate object and the target position based on the historical work content of the candidate object and the position description information. determine an industry description matching degree between the candidate object and the target position based on the historical work industry of the candidate object and the position description information. determine a second position matching degree between the candidate object and the target position based on the content description matching degree and the industry description matching degree between the candidate object and the target position.

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