Skill matching algorithm based on big data analysis

Abstract

The invention discloses a skill matching algorithm based on big data analysis, and the algorithm specifically comprises the following steps: S1, enterprise data collection: obtaining position data information of a recruitment enterprise from a vocational information base of multiple channels, and carrying out the preprocessing of the position data information to obtain position basic data; the invention relates to the technical field of big data analysis. According to the skill matching algorithm based on big data analysis, basic data are obtained through collection of enterprise user data and individual user data, key features in the basic data are extracted, then data modeling is carried out in cooperation with a machine learning algorithm, the extracted key features are quantified into computable indexes, and the skill matching algorithm based on big data analysis is obtained. According to the method, enterprise users and individual users are matched through a machine learning algorithm, and the accuracy and efficiency of matching of occupations and users are improved by continuously optimizing a recommendation sequence, so that enterprises and job seekers can be helped to find matched positions and talents more efficiently, and the recruitment and job seeking efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a skill matching algorithm based on big data analysis. Background Art

[0002] The reference patent name is: A skill matching algorithm based on big data analysis (patent publication number: CN118428792A, patent publication date: 2024.08.02), which solves the technical problem in the prior art that it is difficult to accurately and quickly match positions with job seekers and provide a development plan for the profession; the present invention classifies personal standard data according to occupational data to obtain candidates for carry-in and candidates for non-carry-in, which can reduce the amount of data that needs to be calculated; the occupational matching degree of the carry-in candidates is obtained by calculating the cosine similarity, and the skill coefficient of the candidate is obtained based on the occupational matching degree, and the selected candidate is selected according to the size of the skill coefficient, which can more accurately obtain the selected candidate; the occupational skill requirements and occupational development skills are obtained through the occupational skill model, and the occupational development plan is obtained according to the occupational development skills; the unsuccessful candidates can more intuitively understand the skills that need to be improved, and can carry out learning and training in a more targeted manner.

[0003] Based on the above document: With the increasing demand for recruitment and job hunting, the globalization of information and the rapid development of the Internet have brought about a new job hunting and recruitment model - online recruitment. Various recruitment websites have emerged in an endless stream, and networked social methods have also provided a good human resources platform. Due to the richness of information on the online recruitment platform and the convenience of operation, more and more job seekers search for suitable recruitment information through the online recruitment platform. Job seekers send their resumes, and the recruiting units use the matching algorithm of the service platform to determine whether the collected resumes match the recruiting positions, and then consider whether to hire them. However, since the existing matching algorithms are relatively single and simple, the matching values ​​obtained cannot guarantee high accuracy, which affects the efficiency and accuracy of talent recruitment and career recommendations. To this end, the present invention provides a skill matching algorithm based on big data analysis. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a skill matching algorithm based on big data analysis, which solves the problem that the existing matching algorithms are relatively single and simple, and the resulting matching values ​​cannot guarantee high accuracy, thus affecting the efficiency and accuracy of talent recruitment and career recommendations.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a skill matching algorithm based on big data analysis, specifically comprising the following steps:

[0006] S1. Enterprise data collection: Obtain the job data information of recruiting enterprises from the career information database of multiple channels, and obtain the basic job data after pre-processing;

[0007] S2. Personal data collection: obtaining job search data information in the resumes submitted by job seekers from job search information databases of multiple channels, and obtaining basic personal data after pre-processing;

[0008] S3, feature extraction: extract key features from the collected position basic data and personal basic data;

[0009] S4. Data modeling: Build mathematical models of individual users and corporate positions, and quantify the key features extracted above into computable indicators;

[0010] S5. Algorithm matching: Use machine learning algorithms to match and classify basic position data and some basic personal data;

[0011] S6. Result optimization: Based on the matching results, the algorithm optimizes the recommendation order to improve the accuracy and efficiency of matching positions and users;

[0012] S7. Feedback mechanism: The feedback collected from users and enterprises is used for free learning and optimization of the algorithm.

[0013] Preferably, the basic position data in S1 mainly includes: position description, job requirements, educational requirements, skill requirements, working hours, salary and working location.

[0014] Preferably, the basic personal data in S2 mainly includes: personal information, educational background, work experience, skill certificates, expected salary and geographical location.

[0015] Preferably, the preprocessing in S1-S2 mainly includes: data cleaning and data standardization.

[0016] Preferably, the key features in S3-S4 include: personal skills, work experience, educational background, enterprise needs, job requirements and geographical location.

[0017] Preferably, the machine learning algorithm in S5 is: a decision tree algorithm, a neural network algorithm and a collaborative filtering algorithm.

[0018] Preferably, in S5-S6, the trained model is used to perform preliminary matching between users and positions, and the algorithm parameters are adjusted according to the matching results and user feedback to optimize the matching effect.

[0019] Preferably, the S7 continuously updates the model through online learning to adapt to data changes and user needs, and continuously optimizes the recommendation algorithm and improves user experience by collecting user usage habits and feedback information.

[0020] Beneficial Effects

[0021] The present invention provides a skill matching algorithm based on big data analysis. Compared with the prior art, it has the following beneficial effects:

[0022] (1) This skill matching algorithm based on big data analysis obtains basic data by collecting enterprise user data and personal user data, and extracts key features from the basic data. Subsequently, it uses a machine learning algorithm to perform data modeling and quantifies the extracted key features into computable indicators. It matches enterprise users and personal users through a machine learning algorithm, and continuously optimizes the recommendation order to improve the accuracy and efficiency of matching occupations with users, thereby helping enterprises and job seekers to find matching positions and talents more efficiently and improving the efficiency of recruitment and job hunting. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a process flow chart of the skill matching algorithm for big data analysis of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 The present invention provides a technical solution: a skill matching algorithm based on big data analysis, which specifically includes the following steps:

[0026] S1. Enterprise data collection: Obtain the job data information of recruiting enterprises from the career information database of multiple channels, and obtain the basic job data after pre-processing;

[0027] Collect data related to skill supply and demand from multiple channels, including but not limited to personal resumes, company job descriptions, and industry trend analysis;

[0028] S2. Personal data collection: obtaining job search data information in the resumes submitted by job seekers from job search information databases of multiple channels, and obtaining basic personal data after pre-processing;

[0029] S3, feature extraction: extract key features from the collected position basic data and personal basic data;

[0030] S4. Data modeling: Build mathematical models of individual users and corporate positions, and quantify the key features extracted above into computable indicators;

[0031] S5. Algorithm matching: Use machine learning algorithms to match and classify basic position data and some basic personal data;

[0032] S6. Result optimization: Based on the matching results, the algorithm optimizes the recommendation order to improve the accuracy and efficiency of matching positions and users;

[0033] S7. Feedback mechanism: The feedback collected from users and enterprises is used for free learning and optimization of the algorithm;

[0034] The specific steps of the algorithm are as follows:

[0035] 1. Data preprocessing: remove duplicate, erroneous or incomplete data through data cleaning, and convert data of different dimensions into comparable formats through data standardization;

[0036] 2. Feature engineering: Different categories of skill atmosphere, such as programming language, management skills, and professional skills, as well as factors such as user skills and experience, are converted into vector form;

[0037] 3. Model construction: You can select the appropriate machine learning algorithm based on the characteristics and needs of the data, and use the labeled data set to train the model to learn the matching rules between users and positions;

[0038] 4. Skill matching: Use the trained model to initially match users and positions. Then, based on the matching results and user feedback, adjust the algorithm parameters to optimize the matching effect.

[0039] 5. Recommendation result output: Sort the positions according to the matching scores, give priority to recommending positions with high scores, and continuously optimize the algorithm by continuously collecting user feedback on the recommendation results;

[0040] 6. Iterative optimization: Through online learning, the model is continuously updated to adapt to data changes and user needs, and the recommendation algorithm is continuously optimized based on user usage habits and feedback to improve user experience;

[0041] The present invention: The basic position data in S1 mainly includes: position description, position requirements, educational requirements, skill requirements, working hours, salary and working location;

[0042] The present invention: The basic personal data in S2 mainly includes: personal information, educational background, work experience, skill certificates, expected salary and geographical location;

[0043] In the present invention, the preprocessing in S1-S2 mainly includes: data cleaning and data standardization;

[0044] Data cleaning: remove duplicate, erroneous and incomplete data;

[0045] Data standardization: converting data of different dimensions into comparable formats;

[0046] The present invention: The key features in S3-S4 include: personal skills, work experience, educational background, corporate needs, job requirements and geographical location;

[0047] The present invention: the machine learning algorithm in S5 is: decision tree algorithm, neural network algorithm and collaborative filtering algorithm;

[0048] Decision tree algorithm: It is a commonly used data mining and classification technology. It represents a set of decisions by constructing a tree structure. Each decision has certain judgment criteria to achieve data classification, identification and mining. Its advantages are high classification accuracy, simple and easy-to-understand generated patterns, good robustness to noisy data, short training time and visualization advantages.

[0049] The basic steps of the decision tree algorithm are as follows:

[0050] Construct the root node: put all training data in the root node, select an optimal feature, and divide the training data set into subsets according to this feature;

[0051] Recursive construction: Repeat the above process for each subset to form each branch of the decision tree until the stopping condition is met (such as all samples belong to the same class and there are no remaining attributes that can be further divided);

[0052] Pruning: Test, correct and prune the generated decision tree to improve the generalization ability of the model and prevent overfitting;

[0053] Neural network algorithm: It is a machine learning algorithm inspired by the physiological structure of the human brain. It realizes data processing and pattern recognition by simulating the connection and information transmission process between neurons. The neural network consists of an input layer, a hidden layer, and an output layer. The connection weights between layers are adjusted through training to achieve the desired output results.

[0054] The characteristics of the neural network algorithm are as follows:

[0055] Distributed storage and parallel processing: Neural networks achieve distributed storage and parallel processing of information through a large number of neuron connections, which improves processing speed and efficiency;

[0056] Self-learning and self-adaptation: Neural networks have the ability to self-learn and adapt, and can automatically adjust the network structure and weights according to training data to adapt to different tasks and scenarios;

[0057] Nonlinear mapping capability: Neural networks can approximate any nonlinear function and have powerful nonlinear mapping capabilities, which are suitable for solving complex problems;

[0058] Collaborative filtering algorithm: It is a recommendation algorithm based on user historical behavior data. It recommends products by mining user preferences and predicting the products that users may like. It is mainly divided into two types: user-based collaborative filtering and item-based collaborative filtering;

[0059] User-based collaborative filtering: The user-based collaborative filtering algorithm calculates the similarity between users, finds users similar to the target user, and recommends items that these similar users like to the target user. In the implementation process, it is necessary to solve the problem of how to calculate the similarity between users. Common calculation methods include Jaccard similarity coefficient, angle cosine, etc.

[0060] Item-based collaborative filtering: The item-based collaborative filtering algorithm calculates the similarity between items, finds items similar to the target item, and recommends these similar items to users who like the target item. This method is suitable for scenarios where the number of items is much larger than the number of users, and the amount of calculation is relatively small;

[0061] The present invention: In S5-S6, the trained model is used to perform preliminary matching between users and positions, and the algorithm parameters are adjusted according to the matching results and user feedback to optimize the matching effect;

[0062] The present invention: S7 continuously updates the model through online learning to adapt to data changes and user needs, and continuously optimizes the recommendation algorithm and improves user experience by collecting user usage habits and feedback information.

[0063] To sum up: this skill matching algorithm based on big data analysis obtains basic data by collecting enterprise user data and personal user data, and extracts key features from the basic data. Subsequently, it cooperates with machine learning algorithms to perform data modeling, and quantifies the extracted key features into computable indicators. It matches enterprise users and personal users through machine learning algorithms, and improves the accuracy and efficiency of matching occupations with users by continuously optimizing the recommendation order, thereby helping enterprises and job seekers to find matching positions and talents more efficiently, and improving the efficiency of recruitment and job hunting.

[0064] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0065] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0066] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A skill matching algorithm based on big data analysis, characterized in that: The specific steps include: S1. Enterprise data collection: Obtain the job data information of recruiting enterprises from the career information database of multiple channels, and obtain the basic job data after pre-processing; S2. Personal data collection: obtaining job search data information in the resumes submitted by job seekers from job search information databases of multiple channels, and obtaining basic personal data after pre-processing; S3, feature extraction: extract key features from the collected position basic data and personal basic data; S4. Data modeling: Build mathematical models of individual users and corporate positions, and quantify the key features extracted above into computable indicators; S5. Algorithm matching: Use machine learning algorithms to match and classify basic position data and some basic personal data; S6. Result optimization: Based on the matching results, the algorithm optimizes the recommendation order to improve the accuracy and efficiency of matching positions and users; S7. Feedback mechanism: The feedback collected from users and enterprises is used for free learning and optimization of the algorithm.

2. A skill matching algorithm based on big data analysis according to claim 1, characterized in that: The basic position data in S1 mainly includes: job description, job requirements, educational requirements, skill requirements, working hours, salary and work location.

3. The skill matching algorithm based on big data analysis according to claim 1 is characterized in that: The basic personal data in S2 mainly includes: personal information, educational background, work experience, skill certificates, expected salary and geographical location.

4. The skill matching algorithm based on big data analysis according to claim 1 is characterized in that: The preprocessing in S1-S2 mainly includes: data cleaning and data standardization.

5. The skill matching algorithm based on big data analysis according to claim 1 is characterized in that: The key features in S3-S4 include: personal skills, work experience, educational background, corporate needs, job requirements and geographical location.

6. The skill matching algorithm based on big data analysis according to claim 1 is characterized in that: The machine learning algorithms in S5 are: decision tree algorithm, neural network algorithm and collaborative filtering algorithm.

7. The skill matching algorithm based on big data analysis according to claim 1 is characterized in that: In S5-S6, the trained model is used to perform preliminary matching between users and positions, and the algorithm parameters are adjusted according to the matching results and user feedback to optimize the matching effect.

8. The skill matching algorithm based on big data analysis according to claim 1 is characterized in that: The S7 continuously updates the model through online learning to adapt to data changes and user needs, and continuously optimizes the recommendation algorithm and improves user experience by collecting user usage habits and feedback information.

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