An ai intelligent recruitment matching recommendation method and system based on dynamic images

Abstract

This invention relates to the field of intelligent recruitment technology, specifically to an AI-powered intelligent recruitment matching and recommendation method and system based on dynamic profiles. The method includes: constructing a dynamic job competency model based on a knowledge graph of industry-related skill standards, and a dynamic talent profile integrating static resumes and multimodal AI interview evaluation results; employing an attention-based interpretable deep matching algorithm to calculate the matching degree between the two in a multidimensional vector space, and generating matching interpretation information with specific supporting evidence; furthermore, based on subsequent enterprise operations and talent onboarding performance data, continuous learning and optimization are achieved through a federated learning framework to update model parameters. This invention achieves a leap from static to dynamic recruitment evaluation, from black-box matching to interpretable matching, and from a fixed system to a self-learning system, significantly improving the accuracy, transparency, and adaptability of talent matching while protecting data privacy.

Description

Technical Field

[0001] This invention relates to the field of intelligent recruitment technology, specifically to an AI-based intelligent recruitment matching and recommendation method and system based on dynamic profiles. Background Technology

[0002] In today's talent recruitment field, traditional resume screening and matching methods are increasingly revealing their limitations. Mainstream methods rely primarily on keyword matching of job descriptions and resume text. This approach is inherently static and superficial, failing to delve into the complex composition of required skills or comprehensively assess a candidate's true potential, especially in areas difficult to quantify such as soft skills and overall competence. While artificial intelligence (AI) technology has been introduced into the recruitment process in recent years, for example, using AI for initial resume screening or automated video interviews, these applications often suffer from fragmentation. On one hand, the constructed job and talent profiles are typically static and isolated, lacking integration with industry dynamics, team-specific preferences, and real-time behavioral performance during interviews. On the other hand, existing matching algorithms often exist as "black boxes," outputting only a matching score without providing understandable decision-making basis, making it difficult for HR managers to trust and effectively utilize AI recommendations. More importantly, existing systems generally lack self-optimization capabilities, failing to continuously learn from subsequent hiring decisions and post-employment performance, leading to model rigidity and difficulty adapting to the unique cultures of different companies and rapidly changing market demands. Therefore, how to build an intelligent recruitment matching system that can dynamically evolve, deeply evaluate, transparently match, and autonomously iterate, while ensuring data privacy and security, has become a key technical challenge that the industry urgently needs to solve.

[0003] Therefore, existing technologies still need further development. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide an AI-based intelligent recruitment matching and recommendation method and system based on dynamic profiles to solve the problems existing in the prior art.

[0005] To achieve the above-mentioned technical objectives, according to a first aspect of the present invention, the present invention provides an AI-based intelligent recruitment matching and recommendation method based on dynamic profiles, comprising: S1. Construct a dynamic job competency model for the enterprise. The dynamic job competency model is a structured model generated based on job description text and associated with an industry skill standard knowledge graph. S2. Construct a dynamic talent profile of the candidate, which is generated by integrating the candidate's static resume information with the dynamic ability assessment results output from the AI ​​interview system. S3. Based on an interpretable deep matching algorithm, calculate the matching degree between the dynamic job competency model and the dynamic talent profile in the multi-dimensional vector space, and generate recommendation results containing matching degree interpretation information; S4. Based on the enterprise's subsequent operational data regarding the recommendation results and the candidate's performance feedback data after joining the company, optimize the parameters of the dynamic capability assessment model and the deep matching algorithm through a continuous learning mechanism.

[0006] Specifically, the construction of the enterprise's dynamic job competency model includes: The job description text is structured and parsed to extract key elements such as core responsibilities, essential skills, and expected traits. From the industry skill standard knowledge graph, standard skill nodes, skill hierarchy relationships, and logical dependencies between skills associated with the key elements are obtained. Based on the key elements and their associated graph information, a structured capability framework including skill dimension, experience dimension, potential dimension, and team culture adaptation dimension is generated as the initial dynamic job capability model.

[0007] Specifically, the knowledge graph of industry-related skill standards also includes: Based on the company's industry, size, and historical recruitment data, the relevant industry-wide general skill standards and company-specific skill preferences are dynamically matched and weighted from the knowledge graph. The company-specific skill preferences are obtained by profiling and analyzing the group of successful candidates recruited by the company's team in the past, so that the generated dynamic job competency model has both industry universality and company specificity.

[0008] Specifically, the construction of a dynamic talent profile for candidates includes: The process involves analyzing a candidate's static resume information to extract structured data related to their educational background, work experience, project experience, and skill certificates; obtaining dynamic competency assessment results output by an integrated AI interview system, which include at least scores for logical thinking and communication skills derived from interview question and answer text analysis, and scores for resilience and teamwork tendency derived from multimodal analysis of interview videos; and aligning and integrating the structured data with the dynamic competency assessment results, mapping them to a unified assessment dimension system to form the dynamic talent profile.

[0009] Specifically, the generation of the dynamic capability assessment results also includes: During the AI ​​interview process, the system records and analyzes in real time the candidate's answers to preset scenario questions and behavioral interview questions, as well as changes in voice tone and facial micro-expressions. Using pre-trained natural language processing and computer vision models, feature vectors related to soft skills are extracted from text, speech, and visual modalities, respectively. The feature vectors are integrated and analyzed through a multimodal fusion network to quantify and output the candidate's evaluation scores on multiple soft skill dimensions, serving as an important supplement to the hard skill information in the static resume.

[0010] Specifically, the calculation of the matching degree based on the interpretable deep matching algorithm includes: The dynamic job competency model and the dynamic talent profile are encoded into feature vectors with the same dimension. The relevance weights of the two feature vectors in each dimension are calculated through an attention mechanism network, and the overall matching score is calculated based on the weighted vector similarity. At the same time, the visual attention weight map generated by the attention mechanism network is used to identify the key dimensions that contribute the most to the overall matching score, which serves as the basis for interpreting the matching score.

[0011] Specifically, generating the matching degree interpretation information also includes: Based on the key dimensions determined by the attention weight map, specific descriptive text or data fragments are extracted from the corresponding dimension requirements of the dynamic job competency model and the corresponding dimension evaluation results of the dynamic talent profile. Based on a predefined interpretation template, the key dimensions, the dimension requirements, the evaluation results, and the sources from which the evaluation results are generated are correlated and combined to generate matching interpretation statements in natural language. The sources from which the interpretations are generated include, but are not limited to, specific questions in AI interviews, fragments of candidates' answers, or conclusions from multimodal analysis.

[0012] Specifically, the optimization based on the company's subsequent operational data regarding the recommendation results and the candidate's performance feedback data after joining the company, through a continuous learning mechanism, includes: Collect the entire process logs of candidate screening, interviewing, and hiring decisions from the enterprise side, as well as the anonymized periodic performance evaluation data of candidates after they have joined the company; use the operation logs and performance feedback data as environmental feedback signals for reinforcement learning to construct a reward function aimed at improving the long-term talent matching success rate; and iteratively update the feature extraction network parameters used to extract features in the dynamic ability assessment model and the neural network parameters used to calculate weights and similarities in the deep matching algorithm through the policy gradient method to achieve self-iterative optimization of the assessment and matching model.

[0013] Specifically, the continuous learning mechanism is implemented using a federated learning framework, including: On the client deployed locally within the enterprise, the local model is trained using the enterprise's anonymized post-operation data and performance feedback data to generate model parameter update gradients. These gradients are then encrypted and uploaded to a central server. The central server aggregates encrypted gradients from multiple enterprises, updates the global model parameters using a secure aggregation algorithm, and distributes the updated global model parameters to each client. Each client uses these global model parameters to update its local model, thereby enabling the optimization of the global recruitment matching model's performance using cross-enterprise data while protecting the data privacy of each enterprise.

[0014] According to a second aspect of the present invention, an AI-powered intelligent recruitment matching and recommendation system based on dynamic profiles is provided, comprising: The dynamic profile building module is used to perform the steps of building a dynamic job competency model and a dynamic talent profile. The intelligent matching and recommendation module is used to perform the steps of calculating the matching degree and generating recommendation results; The continuous learning optimization module is used to perform the steps described above for optimizing model parameters through a continuous learning mechanism.

[0015] Beneficial effects: The AI-powered intelligent recruitment matching and recommendation method and system based on dynamic profiles provided by this invention can produce a series of significant beneficial effects compared with existing technologies.

[0016] First, by constructing a dynamic job competency model that deeply integrates industry knowledge graphs and enterprise-specific preferences, and a dynamic talent profile that integrates static resumes and multimodal AI interview results, a holographic and structured representation of both recruiters and recruiters, from surface characteristics to deep capabilities, is achieved. This greatly enhances the depth, breadth, and standardization of the assessment, laying a solid foundation for accurate matching.

[0017] Secondly, the interpretable deep matching algorithm used reveals the multidimensional relationship between job requirements and talent capabilities through the attention mechanism. It not only calculates the accurate matching degree, but also generates natural language interpretations with clear evidence, making the AI ​​decision-making process transparent. This greatly enhances human resource managers' trust in the recommendation results and their willingness to adopt them, and can provide precise questioning directions for subsequent interviews.

[0018] Furthermore, the continuous learning optimization mechanism created in this invention, particularly the implementation based on a federated learning framework, constructs a complete data closed loop from recommendation and recruitment to performance feedback. This system can utilize the actual recruitment results and talent performance data of each enterprise, driving continuous self-iteration and optimization of model parameters while strictly protecting enterprise data privacy. This enables the entire system to possess an adaptive capability that becomes increasingly accurate with use, gradually fitting the unique talent success standards of different enterprises, significantly improving the long-term fit between talent and positions, and reducing the risk of recruitment failures and talent turnover.

[0019] Finally, the entire technical solution forms a synergistic and enhanced organic whole. Dynamic profiling provides high-quality input for accurate matching, interpretable matching provides understandable feedback for continuous learning, and continuous learning, in turn, continuously optimizes the profiling and matching algorithms, thereby systematically and sustainably improving the overall effectiveness and value of intelligent recruitment. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the AI-powered intelligent recruitment matching and recommendation method based on dynamic profiling provided in a specific embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0022] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0023] Please see Figure 1 This invention provides an AI-powered intelligent recruitment matching and recommendation method based on dynamic profiles, comprising: S1. Construct a dynamic job competency model for the enterprise. The dynamic job competency model is a structured model generated based on job description text and associated with an industry skill standard knowledge graph.

[0024] It should be further noted that the implementation of this method begins with the company's HR creating a job in the system.

[0025] When constructing a dynamic job competency model, the job description text is first subjected to deep semantic parsing. A pre-trained BERT model is used to encode the text, identify and extract entities and relationships such as "core skills," "job descriptions," "experience requirements," and "qualification requirements." For example, from "responsible for backend service development, proficient in Java, with experience in high-concurrency processing, and possessing good communication skills," the entities are extracted as: <skills, Java>, <concepts, high concurrency>, and <soft skills, communication skills>, and the relationships are established as: <job, requirements, skills (Java)> and <job, requirements, concepts (high concurrency)>.

[0026] Next, the extracted entities are linked and associated with a pre-built industry skills standard knowledge graph. This knowledge graph is stored in RDF format and contains millions of entities such as "skill-job-course-certification" and relationships such as "belongs to," "prerequisite," and "related." For example, the entity "Java" is linked to the standard skill node "Java programming language" in the graph, and its associated parent node "programming language," lower nodes "Spring framework" and "JVM," and its "usually paired" skills "MySQL" and "Linux" are obtained. Based on the centrality of the skill node in the knowledge graph, its industry relevance to the target job, and the frequency and position of the skill in the job description (e.g., "proficient" is given higher weight), the system calculates a quantitative requirement intensity value for the "Java" skill. The calculation formula can be: .in, It is the normalized word frequency. The weights are based on location ("Job Responsibilities" is 1.0, "Job Requirements" is 0.8, and "Preferred Qualifications" is 0.5). It is the degree of association between the skill node and the target job type in the graph (calculated from the relationship strength of the edges in the graph).

[0027] Ultimately, all identified skills, years of experience, and required qualities are categorized according to a unified dimensional framework (e.g., six core dimensions: technical ability, business understanding, communication and collaboration, logical thinking, stress resistance, and leadership potential), and assigned calculated quantitative weights to form a structured, quantifiable, and dynamic job competency model vector. ,in This indicates the required intensity of the position in the i-th dimension, where d is the total number of dimensions, preferably 6-10, because it covers the main evaluation aspects while avoiding the curse of dimensionality.

[0028] S2. Construct a dynamic talent profile of the candidate, which is generated by integrating the candidate's static resume information with the dynamic ability assessment results output from the AI ​​interview system.

[0029] It should be further explained that when constructing the dynamic talent profile of a candidate, static resume information is parsed into structured JSON data using OCR and NER models. Simultaneously, the candidate enters an integrated AI interview process. The AI ​​interview system has a pre-set question bank including behavioral interviews, situational simulations, and quick technical questions. During the interview, the system collects the candidate's audio, video, and transcribed text in real time. Dynamic ability assessment is achieved through a multimodal fusion model: the text modality uses the RoBERTa model to extract semantic vectors and logical structure features of the answers; the audio modality extracts acoustic features such as MFCC, speech rate, and fundamental frequency, which are then input into an LSTM network to analyze emotional stability; the video modality uses the Open Face library to extract facial action unit sequences and analyze micro-expression changes. These multimodal features are concatenated in a later fusion layer and input into a multilayer perceptron, outputting the candidate's scores in several soft skill dimensions (such as communication, logical thinking, stress resistance, and teamwork).

[0030] S3. Based on an interpretable deep matching algorithm, calculate the matching degree between the dynamic job competency model and the dynamic talent profile in the multi-dimensional vector space, and generate recommendation results containing matching degree interpretation information.

[0031] It should be further explained that the statically parsed hard indicators such as skill level and project experience duration, along with the dynamically evaluated soft skill scores, are jointly normalized and mapped to the same d-dimensional space as the job competency model, forming a dynamic talent profile vector. The matching degree calculation employs an interpretable deep matching network based on a multi-head attention mechanism. First, the matching degree is calculated using... and The query vector is obtained by passing each vector through a linear projection layer. Key vector Sum value vector Then calculate the attention weights: ,in is the dimension of the key vector, used as a scaling factor to prevent gradient vanishing. Here, The calculated attention score matrix, specifically the element in the i-th row and j-th column, intuitively reflects the correlation between the i-th dimension of the job requirements and the j-th dimension of the candidate's abilities. Final matching score. The similarity between the aggregated representation of the attention-weighted value vector and the original job vector is calculated. The matching degree interpretation information is obtained by analyzing the attention weight matrix, selecting the top K dimension pairs with the highest weights (K is preferably 3, because it can focus on key points and the interpretation is not verbose), and generating natural language based on the specific descriptions of these dimensions, such as "Candidate A is highly matched with the job requirements in the 'stress resistance' dimension, based on the fact that when faced with stressful situation questions in the AI ​​interview, his speech spectrum was stable and he used positive stress coping strategy vocabulary in his answers."

[0032] S4. Based on the enterprise's subsequent operational data regarding the recommendation results and the candidate's performance feedback data after joining the company, optimize the parameters of the dynamic capability assessment model and the deep matching algorithm through a continuous learning mechanism.

[0033] It should be further explained that the continuous learning mechanism relies on an online reinforcement learning framework. The system treats each recommendation (job-candidate list) as an event, and uses subsequent HR actions (viewing, inviting, issuing offers, hiring) and the candidate's quarterly performance rating after onboarding (normalized to 0-1) as delayed reward signals. The reward function is designed as follows: .in, It is an indicator function (1 if it occurs, 0 otherwise). It's a performance rating. This is the performance reward coefficient, with a preferred value of 1.5, because performance is a key indicator for measuring long-term success and should be given a higher weight. Model parameters The update objective (including the parameters of the feature encoding and matching network) is to minimize the negative expected reward: ,in It is a policy network whose output determines the model's adjustment action 'a' to the feature weights. Through the policy gradient method, the model continuously learns, increasing the probability of generating a matching policy with high rewards (i.e., recommending candidates who are ultimately hired and have good performance).

[0034] Understandably, this method fundamentally changes the traditional keyword-based fuzzy matching recruitment model by constructing a structured, dual-end profile that is dynamic, quantifiable, and correlated with external knowledge. It not only assesses a candidate's "past" (static resume) but also evaluates their "current thoughts and actions" (dynamic capabilities) through AI interviews, providing a comprehensive evaluation. Employing an interpretable deep matching algorithm, it not only outputs matching scores but also reveals the basis for the matching, making the AI ​​decision-making process transparent and credible. HR can then use this interpretation to conduct more targeted interviews or make more informed decisions. Most importantly, this method forms a complete data loop from assessment, matching, decision-making to feedback optimization. Through reinforcement learning, it continuously iterates using real recruitment results and talent performance data, enabling the system to continuously adapt to changing corporate needs and talent market trends. Matching accuracy improves over time, achieving truly intelligent recruitment recommendations.

[0035] Specifically, the construction of a dynamic job competency model for an enterprise includes: performing structured parsing of job description text to extract key elements such as core responsibilities, essential skills, and expected traits; obtaining standard skill nodes, skill hierarchy relationships, and logical dependencies between skills associated with the key elements from the industry skill standard knowledge graph; and generating a structured competency framework containing skill dimensions, experience dimensions, potential dimensions, and team culture adaptation dimensions based on the key elements and their associated graph information, as the initial dynamic job competency model.

[0036] It should be further explained that the structured parsing of job description text involves a pipeline processing procedure: (1) Text cleaning and sentence segmentation: Remove HTML tags and irrelevant symbols, and segment the text into independent sentences.

[0037] (2) Sentence classification: A text classification model (such as TextCNN) finely tuned on recruitment texts is used to classify each sentence into a predefined category, such as “job responsibilities”, “job requirements”, “benefits”, “company introduction”, etc., and the “job responsibilities” and “job requirements” categories are focused on for further in-depth analysis.

[0038] (3) Key element extraction: On the target sentence set, a sequence labeling model based on a pre-trained language model (such as BERT-CRF) is used for named entity recognition. The defined entity types include: (skills, such as "Python", "project management"), (experience, such as "more than three years", "recent graduate"), (education, such as "bachelor's degree"), (certificates, such as "PMP"),<SOFT_TRAIT> (Soft skills, such as "communication skills" and "sense of responsibility") (Responsibilities, such as "responsible for system architecture design"). Simultaneously, dependency parsing identifies modification relationships between entities; for example, in the sentence "proficient in Java programming," it identifies the entities...<SKILL,Java> The modifier "proficient" indicates the degree of mastery.

[0039] (4) Initial Judgment of Relationships and Weights: Initial weights are assigned based on entity type and modification relationships. For example, if a skill entity is modified by "proficient" or "mastered," its initial weight is set to 1.0; if modified by "understand" or "familiar," the weight is set to 0.6; if it appears in "preferred conditions," the weight is set to 0.3. An experience entity such as "5 years or more" is quantified as an experience value of 5. The process of obtaining related information from the industry skill standard knowledge graph is the core of model structuring and knowledge-based processing. After extracting the skill entity "Spring Boot," the system queries this node in the knowledge graph. The information returned by the graph includes: (a) Standard definition: A lightweight Java development framework.

[0040] (b) Superior relationship: successively belonging to "Java framework", "programming framework", and "software development tools".

[0041] (c) Sub-modules: including "Spring MVC", "Spring Data JPA" and other sub-modules.

[0042] (d) Dependencies: Depends on "Java" and "Maven / Gradle".

[0043] (e) Relationships: Often used in conjunction with skills such as "microservices," "Docker," and "Redis." This information greatly enriches the connotation of the skill entity. Generating a structured capability framework is the process of integrating discrete elements and rich graphical information into a unified framework. This framework presupposes four primary dimensions: ① Skills Dimension: Integrating all entities. Each skill not only has its own weight, but also incorporates the influence of related skills through knowledge graph connections. For example, if a job requires "Spring Boot," the weights of strongly related skills like "Microservices" and "Docker" will be appropriately increased (e.g., by adding a base weight of 0.1), even if these terms are not explicitly mentioned in the job description. The final score for the skills dimension is a weighted sum of all skills and their related skills.

[0044] ② Experience Dimension: Integration and Entity. Descriptive experience requirements (e.g., "3-5 years") are quantified into a numerical range [3, 5]. The system may use the median of this range, 4, as the experience value. Education level is mapped to a rating scale, for example: PhD - 5, Master's - 4, Bachelor's - 3, Associate's - 2. The final value of the experience dimension is a composite score that integrates years of work experience, depth of project experience (inferred from the job description), and educational background.

[0045] ③ Potential Dimension: This dimension does not rely entirely on the job description text, but is inferred by analyzing the "cutting-edge" and "transferability" of nodes in the skills graph. For example, for the skill "Python," the knowledge graph may mark its "popularity" index and "learning curve" index. The system calculates the average "cutting-edge" and average "learning curve" of all skills required for the position, and combines this with the job type (e.g., "R&D" positions have higher potential weights) to assess the potential requirements of the position for the candidate's learning potential and adaptability to future changes.

[0046] ④ Team culture fit dimension: Through analysis<SOFT_TRAIT> The system constructs a cultural vector based on common characteristics of entities, company introduction texts, and team history members (if available). For example, if the job description frequently mentions "entrepreneurial spirit" and "rapid iteration," while the company introduction emphasizes "flat management," the system will construct a cultural vector that includes traits such as "risk-taking tendency," "adaptability to change," and "weak hierarchical concept." The construction of this dimension relies heavily on NLP sentiment analysis and topic modeling techniques. These four vectors are concatenated and passed through a normalization layer to ultimately form the initial dynamic job competency model vector.

[0047] Understandably, through this structured parsing method that deeply integrates knowledge graphs, the dynamic job competency model is no longer a simple list of keywords, but a structured competency framework containing rich semantic relationships and industry background knowledge. It can transform vague, non-standard job description language into standardized, computable competency dimension vectors. This not only eliminates the subjectivity and inconsistency caused by different HR professionals writing job descriptions, but also lays the foundation for accurate, multi-dimensional matching with candidate competency profiles. More importantly, by introducing knowledge graphs, the model gains an industry perspective, automatically associating and supplementing relevant skills and requirements that are not explicitly stated in the job description but are actually important. This makes the job model more comprehensive and intelligent, providing a solid, standardized benchmark for high-quality talent matching.

[0048] Specifically, the related industry skill standard knowledge graph also includes: dynamically matching and weighting the relevant industry-wide skill standards and company-specific skill preferences from the knowledge graph based on the company's industry, size, and historical recruitment data. The company-specific skill preferences are obtained by profiling and analyzing the group of successful candidates recruited by the company's team in the past, so that the generated dynamic job competency model has both industry universality and company specificity.

[0049] It should be further explained that the dynamic weighting mechanism, which integrates industry-wide applicability with enterprise-specific characteristics, includes the following steps in its implementation: (a) Construction and Matching of Industry-Specific Skill Standards Library: In the backend, a continuously updated industry-specific skills standards library is maintained. This library is essentially a subview of a knowledge graph, where each skill node is tagged with one or more industry labels (e.g., "Internet - Backend Development", "Finance - Quantitative Analysis", "Manufacturing - Automation"). Simultaneously, each skill node, under different industry labels, has an "Industry Universality Strength" score. This score is calculated based on the frequency of the skill appearing in a large number of job postings within the industry, as well as its co-occurrence frequency with core industry skills. When building a model for a new job, the system first determines its industry category by analyzing company information and job descriptions. Then, it extracts the Top-N high-scoring skills within that industry category from the knowledge graph. The skill nodes and their standard descriptions constitute the "industry benchmark skills set" for this position. For example, for the "Internet - Backend Development" position, This may include {Java, Python, database principles, data structures, Linux, network protocols, ...}. The initial weights of these skills... By its The score determines the outcome.

[0050] (b) Enterprise-Specific Skill Preference Mining: After obtaining authorization from the enterprise, the system analyzes the resumes and evaluation files of all candidates successfully hired (defined as those who passed the probationary period and whose first annual performance was good or above) within a specific team of that enterprise (e.g., "XX Company - Advertising Recommendation Algorithm Group") over a past period (e.g., the past 24 months). Cluster analysis and association rule mining are performed on the skill tags in these files. The specific operation is as follows: ① Extract the skill list of all successful candidates to form a transaction database.

[0051] ② Use the FP-Growth algorithm to mine frequent itemsets.

[0052] ③ Set a minimum support threshold (e.g., 0.3, representing a skill combination possessed by 30% of successful candidates) and a minimum confidence level to uncover strong association rules. For example, it might uncover rules {"Python", "TensorFlow"} and {"success"} with a confidence level as high as 85%. This means that within this team, proficiency in both Python and TensorFlow is strongly correlated with successful hiring. These skills within frequent itemsets or strong association rules constitute a "company-specific skill preference set". .for For each skill, calculate its "enterprise-specific strength" score. The score is determined by the frequency of its appearance among successful candidates and its degree of difference from the industry benchmark.

[0053] (c) Dynamic weighted fusion to generate the final model: When generating the final dynamic job competency model vector, for each skill dimension (or a finer-grained skill node), its final weight is... It is a weighted sum of industry benchmark weights and firm preference weights. The specific formula is as follows: .in, This is the industry benchmark weight; if the skill is not... If the value is in the middle, then this item is 0. It is the enterprise preference weight; if the skill is not... If the value is in the middle, then this item is 0. It is the harmonic coefficient, whose value is dynamically adjusted between 0 and 1. The initial value is set to 0.7 because there is insufficient historical data for the company initially, and it should rely more on industry-standard data. As the company accumulates historical recruitment data, the system will evaluate the predictive power of the company's preference data. The evaluation method could be: using different... The generated job model is used to match historical successful candidates to see which one... The matching degree is higher under certain values. The system can adjust this automatically or manually by the administrator. When enterprise data is of high quality and has strong predictive power, This can be reduced to 0.5 or even lower, giving higher weight to enterprise-specific preferences. Furthermore, for skills explicitly mentioned in the job description, their weight will be... Multiply by a magnification factor on top of that. ( (e.g., 1.2), to respect the HR's explicit intent.

[0054] Understandably, this dynamic weighted fusion mechanism brings significant benefits. First, it resolves the contradiction between standardization and personalization. Industry benchmarks ensure the model's professionalism and universality, avoiding biases or limitations in perspective that might arise from a company's own preferences; while the introduction of company-specific preferences allows the model to accurately capture the unique and subtle "tastes" of specific teams and business lines for talent—tastes that are often tacit knowledge derived from the company's successful experiences. Second, the mechanism is adaptive. For newly established teams or business lines, the system primarily relies on industry benchmarks to provide a reliable starting point. As the team recruits more employees and generates performance data, the system can automatically learn and strengthen the skill sets that truly lead to success, causing the job model to continuously "evolve" and become increasingly aligned with the team's actual success standards. This is equivalent to solidifying the tacit experience of the team's best recruiters into the AI ​​model, achieving knowledge accumulation and inheritance, and greatly improving the accuracy of recruitment and the long-term success rate of person-job matching.

[0055] Specifically, constructing a dynamic talent profile for candidates includes: parsing the candidate's static resume information and extracting structured data related to educational background, work experience, project experience, and skill certificates; obtaining dynamic ability assessment results output by an integrated AI interview system after evaluating the candidate, wherein the dynamic ability assessment results include at least scores for logical thinking ability and communication skills derived from interview question and answer text analysis, and scores for stress resistance and cooperation tendency derived from multimodal analysis of interview videos; aligning and integrating the structured data with the dynamic ability assessment results, mapping them to a unified assessment dimension system to form the dynamic talent profile.

[0056] It should be further explained that static resume information parsing is a complex process of information extraction and standardization: 1. Document parsing and text extraction: Receive resumes in PDF, Word and other formats, and use libraries such as Apache PDF Box, Python-docx or commercial OCR services (such as models optimized for resumes) to extract the original text while retaining basic formatting information (such as paragraphs and bold text).

[0057] 2. Block Segmentation and Classification: Using rules or lightweight classification models based on layout analysis and text features (such as chapter titles and keywords), the resume text is segmented and classified into predefined blocks, such as "Personal Information", "Education Background", "Work Experience", "Project Experience", "Professional Skills", "Certificates and Honors", etc.

[0058] 3. Structured information extraction: ① Educational Background: Regular expressions and a NER model are used to identify school name, degree level, major, and start and end dates. Standardization is achieved by linking to a school-major database; for example, "Computer Science and Technology," "Software Engineering," and "Computer Science" are uniformly mapped to "Computer-related." Educational background scores are calculated based on school ranking (within compliance requirements) and degree level. .

[0059] ② Work Experience: This is the most challenging part of the parsing. A pre-trained language model (such as BERT fine-tuned on a large amount of resume text) is used for sequence labeling to identify the "Company Name," "Position," "Time Period," and "Job Description" for each experience. For the "Job Description" section, a NER model is further used to extract technical entities, project names, and performance figures (such as "improved efficiency by 30%)." Each experience is structured into a JSON object.

[0060] ③ Project Experience: The analysis method is similar to work experience, but it focuses more on the technology stack and specific responsibilities. It is usually extracted from the "Job Description" in "Work Experience" or a separate "Project Experience" section.

[0061] ④ Skills Certificates: Identify the enumerated items in the "Professional Skills" section and link and disambiguate them with standard skill nodes in the knowledge graph. Identify the "Certificates" section and match them with a standard certificate database. The process of obtaining dynamic competency assessment results proceeds sequentially within the integrated AI interview platform: (a) Interview Process and Data Collection: The system presents candidates with a series of questions (text or pre-set video), and candidates answer them within a specified time using a camera and microphone. The system records audio and video throughout the process and converts speech to text in real time (ASR).

[0062] (b) Text Analysis Model: The answer text for each question is analyzed using a finely tuned pre-trained language model (such as DeBERTa). Logical Thinking Ability Scoring The scoring method involves analyzing the text structure of the responses to calculate their coherence and the completeness of causal chains. For example, it can calculate the density and appropriateness of logical connectors (because, therefore, however, firstly, secondly, etc.) in the text, and combine this with a pre-trained model's understanding of the text's logical relationships to assign a score. (Communication and expression skills scoring) The assessment is performed by analyzing language fluency (such as average sentence length and clause complexity), vocabulary richness, and the presence of excessive repetition and filler words. A scoring model trained on speech or dialogue corpora can be used.

[0063] (c) Multimodal analysis model: stress resistance rating The system inserts 1-2 pre-set stress-situation questions into the interview (e.g., "Describe your most unfortunate experience" or "What emergency online failure are you handling?"). While candidates answer these questions, their multimodal signals are analyzed. For speech: features such as fundamental frequency (F0), speech rate, energy, and pause frequency are extracted. Under stress, the fundamental frequency may increase, the speech rate may increase or stutter, and pauses may be abnormal. For video: micro-expressions are analyzed using a Facial Action Coding System (FACS), such as raised eyebrows (AU1+2, possibly indicating surprise / nervousness), tight lips (AU23, possibly indicating stress), and increased blinking frequency. These temporal features are input into an LSTM network and finally output through a fully connected layer. Its value ranges from 0 to 1. Cooperation tendency score. When candidates describe their teamwork experiences, their nonverbal behaviors are analyzed. For example, the frequency of smiling (AU12) when referring to "our team," the frequency of nodding (indicating listening and agreement), and the openness of hand gestures (palms up, outward). These features are also processed through a visual behavior analysis network, outputting... .

[0064] (d) Alignment and Integration: Ultimately, the system maintains a unified evaluation dimension system, such as: [Professional Skills, Project Experience, Educational Background, Logical Thinking, Communication Skills, Stress Tolerance, Collaborative Tendency, Learning Potential]. Staticly analyzed data is mapped to the corresponding dimensions: technical stack and skill certificates are mapped to "Professional Skills," work experience and project experience to "Project Experience," and educational background to "Educational Background." The results of dynamic evaluation are directly mapped to "Logical Thinking," "Communication Skills," "Stress Tolerance," and "Collaborative Tendency." The "Learning Potential" dimension may be determined by the depth and breadth of the candidate's answers to open-ended questions in the dynamic evaluation, as well as the diversity of skills and experience in learning new technologies in the static resume. Each dimension ultimately yields a normalized score, collectively forming the candidate's dynamic talent profile vector. When integrating these assessments, scores from dynamic evaluations are typically given higher weight. For example, in the soft skills dimension, dynamic evaluation results may account for 70% of the weight, while soft skills indirectly inferred from static resume text may only account for 30%.

[0065] Understandably, this dynamic and static deeply integrated profiling method enables a holistic and objective assessment of a candidate's abilities. Static resume analysis provides fundamental data on a candidate's past experiences and skills, forming the basis of the assessment. The dynamic assessment provided by AI interviews, acting like a "stress test" and "behavioral observation," reveals soft skills, thought patterns, and on-the-spot traits that a resume cannot reflect. This is crucial for predicting a candidate's future work performance, particularly their adaptability and collaborative abilities. Aligning and integrating these two aspects in a unified dimensional space transforms the candidate assessment from a fragmented "skills list" and "interview impression" into a complete, coherent, and quantifiable "digital twin." This provides a comprehensive and high-quality data foundation for subsequent accurate and interpretable matching with standardized job models, significantly reducing the risk of misjudgment due to incomplete information or subjective bias.

[0066] Specifically, the generation of the dynamic capability assessment results also includes: During the AI ​​interview process, the system records and analyzes in real time the candidate's answers to preset scenario questions and behavioral interview questions, as well as changes in voice tone and facial micro-expressions. Using pre-trained natural language processing and computer vision models, feature vectors related to soft skills are extracted from text, speech, and visual modalities, respectively. The feature vectors are integrated and analyzed through a multimodal fusion network to quantify and output the candidate's evaluation scores on multiple soft skill dimensions, serving as an important supplement to the hard skill information in the static resume.

[0067] It should be further explained that the complete technical process for multimodal dynamic capability assessment is as follows: (1) Question Pre-setting and Triggering: Each question in the interview question bank is pre-marked with its intent and the soft skills dimension to be assessed. For example, the question "Please share an experience where you had a serious disagreement with a colleague and how you ultimately resolved it" mainly assesses the dimensions of "communication and collaboration" and "conflict resolution". The question "What would you do if the project deadline was suddenly brought forward by a week?" mainly assesses "stress resistance" and "planning". The system intelligently selects a set of questions (such as 5-7) to form an interview based on the needs of the job model.

[0068] (2) Multi-channel synchronous data acquisition: The system records synchronously during the candidate's answer process: (a) Audio stream (16kHz, 16-bit PCM format); (b) Video stream (720p, 30fps); (c) Screen activity (if involving sharing or programming questions), audio and video streams are strictly time-synchronized.

[0069] (3) Feature extraction: ①Text feature extraction ( The ASR engine converts audio to text in real time. For the answer text of each question, a sentence-level pre-trained model (such as Sentence-BERT) is used to extract semantic vectors. Simultaneously, for specific soft skills, a model fine-tuned on corresponding labeled data is used to extract specific features. For example, for "logic," a model fine-tuned on a logical reasoning dataset is used to extract the logical structure feature vector of the text.

[0070] ② Speech feature extraction ( The process extracts low-level acoustic features from the audio, including Mel-frequency cepstral coefficients (MFCC, 13-dimensional, including energy), fundamental frequency (F0), harmonic noise ratio (HNR), and speech intensity. These low-level features are organized into a temporal feature sequence. This sequence is then fed into the intermediate layer of a pre-trained speech emotion recognition model (such as a model trained using the MELD or IEMOCAP dataset) to extract high-level speech emotion representation vectors. Furthermore, global statistical features, such as average speech rate (words / minute), average fundamental frequency, and pause frequency (number of pauses / minute), are calculated and concatenated with the high-level representations.

[0071] ③ Visual feature extraction ( For each frame of the video stream, a face detector (such as MTCNN) is used to locate the face region. Then, a facial action unit (AU) recognition model (such as OpenFace 2.0) is used to extract the intensity values ​​(range 0-5) of 20 core AUs in each frame, forming a 20-dimensional temporal sequence of AU intensity. Simultaneously, a model pre-trained on a facial expression dataset (such as AffectNet) is used to extract facial expression embedding vectors (such as probability distributions of 7 basic emotions) for each frame. The AU intensity sequence and expression embedding sequence are then fed into two independent one-dimensional convolutional neural networks (1D-CNN) for temporal modeling to extract high-level visual behavioral feature vectors, and then the two are concatenated.

[0072] (4) Multimodal fusion and scoring: The feature vectors from the three modalities are fused. A late feature fusion strategy is used here because the features of each modality have already undergone deep processing. First, the text feature vectors are fused... Speech feature vectors Visual feature vectors Dimensionality reduction and alignment are performed using a fully connected layer respectively, resulting in vectors of the same dimension. (Dimension d=128 is an optimal value, balancing information preservation and computational efficiency). Then, an attention-based fusion mechanism is employed. The attention weights for each modality are calculated: in, It is the aligned modal feature vector. and It consists of a learnable weight matrix and a bias vector. It is a learnable weight vector. This is the importance score of the modality. Then, normalized attention weights are obtained using softmax: The weighted fusion of multimodal features is as follows: Finally, The input is a multilayer perceptron (MLP), whose output layer has the same number of neurons as the number of soft skill dimensions to be evaluated (e.g., 4: logical thinking, communication, stress management, and teamwork). A sigmoid activation function is used to map the output of each neuron to the [0,1] interval, serving as the final quantified score for that soft skill dimension. The entire network (the part after the feature extractor) is trained end-to-end using a dataset with manually labeled soft skills, typically employing mean squared error (MSE) or cross-entropy loss as the loss function.

[0073] Understandably, this method of deeply integrating text, voice, and visual multimodal information for assessment significantly improves the accuracy and robustness of the evaluation. Humans are inherently multi-channel when expressing and perceiving soft skills. A single text may mask nervousness, a single voice may fail to reflect logic, and a single facial expression may be deceptive. Multimodal information can corroborate and complement each other. For example, a candidate might claim in text that they are "good at teamwork" (positive textual feature), but if their voice is monotonous and lacks variation when describing teamwork experiences (negative voice feature), and their facial expressions lack interactive smiles or nods (negative visual feature), then the multimodal fusion model is more likely to give a moderate or low "teamwork tendency" score, rather than being misled by the text alone. This multimodal cross-validation mechanism makes the assessment results more comprehensive and objective, closer to the intuitive judgment formed by human interviewers after integrating multiple pieces of information. This effectively compensates for the shortcomings of static resume information, providing unprecedented depth and insight for talent assessment.

[0074] Specifically, the calculation of matching degree based on the interpretable deep matching algorithm includes: encoding the dynamic job competency model and the dynamic talent profile into feature vectors with the same dimension; calculating the relevance weights of the two feature vectors in each dimension through an attention mechanism network, and calculating the overall matching degree score based on the weighted vector similarity; at the same time, the visual attention weight map generated by the attention mechanism network is used to identify the key dimensions that contribute the most to the overall matching degree, serving as the basis for interpreting the matching degree information.

[0075] It should be further explained that the specific mathematical implementation and computational steps of the interpretable deep matching algorithm are as follows: First, the input is a dynamic job competency model vector. and dynamic talent profile vector , where d is the number of evaluation dimensions, for example, d=8. These two vectors may have been normalized so that the value of each dimension is in the range [0,1]. The first step is feature augmentation encoding. To introduce non-linear interactions and improve representational capabilities, we first perform feature augmentation encoding on each dimension. and Perform nonlinear transformation: in, It is a learnable weight matrix. It is a learnable bias vector. is the hidden layer dimension, with an optimal value of 64 to strike a balance between model capacity and computational efficiency. ReLU is the activation function. This yields the enhanced feature representation. and The second step is to calculate the cross-dimensional attention weights. This is the core of achieving interpretability. We calculate the attention score for each dimension of the job to each dimension of the talent, forming an attention matrix. Specifically, for the i-th dimension of the job and the j-th dimension of the talent, the relevance score is calculated: here, yes The feature slices related to the original i-th dimension (since a transformation layer has been applied, they are not directly corresponding here, but for simplicity, they can be regarded as hidden features strongly related to the original i-th dimension) are used in practice. We then use a fully connected layer to... and Mapped to the query and key spaces respectively: , .symbol This indicates vector concatenation. This is a learnable weight vector. LeakyReLU is the activation function, with a negative slope parameter set to 0.2. Then, for each job dimension i, its attention score over all talent dimensions j is normalized to obtain the attention weights: this This constitutes the attention matrix. The element in the i-th row and j-th column visually represents the degree of attention paid by the "requirements in the i-th dimension of the job" to the "abilities in the j-th dimension of the talent". The third step is to calculate the weighted similarity and overall matching degree. First, calculate the basic similarity between the job and the talent in each original dimension. Here, a similarity calculation function that considers tolerance is used: in, and They are and The value in the i-th dimension, It is a very small constant (such as 1e-8) to prevent division by zero. When and When both similarity values ​​are close to 0, this dimension of similarity is meaningless. Therefore, a threshold is set. (The preferred value is 0.1, because it filters out dimensions with very low requirements and no need to pay attention to them.) season Then, using the attention weight matrix, the matching contribution of each dimension i of the position is calculated. This is a variation of weighted summation of the talent's scores across all talent dimensions using the attention weights of that dimension, and then compared with the job requirements. A more direct and effective approach is to use the row sum (or column sum) of the attention matrix as the importance weight for that dimension. A preferred approach is to calculate the mean of the attention weights for each dimension: This represents the average importance of the i-th dimension of the job in the overall match. The overall match score is then calculated. for: Score The value ranges from 0 to 1, with higher values ​​indicating better matching. Meanwhile, the attention matrix... and dimensional importance weights It is saved. When generating the interpretation, select... The top K largest dimensions (K=3), and for each high-weight dimension i, select... The largest talent dimension j forms a key matching pair between "job requirements i" and "talent capabilities j", which serves as the core basis for interpretation.

[0076] Understandably, this algorithm achieves interpretability in the matching process by introducing a cross-dimensional attention mechanism. Traditional cosine similarity or dot product can only provide an overall score, failing to reveal which dimensions play a dominant role. However, the attention weight matrix generated by this algorithm clearly reveals the complex and asymmetric correspondence between job requirements and talent capabilities. For example, the "innovation ability" requirement for a job may primarily focus on the talent's "logical thinking" and "project experience," rather than the literal "innovation ability" score itself. This deep-level correlation mining makes the matching more closely resemble human comprehensive judgment logic. Simultaneously, the interpretation based on attention weights provides an intuitive and quantitative demonstration of the matching basis, greatly enhancing the transparency and credibility of AI recommendation results. This allows HR to not only know "whether it matches" but also "why it matches," enabling faster and more confident recruitment decisions. This interpretation can also serve as a reference for subsequent interview questions.

[0077] Specifically, generating the matching degree interpretation information further includes: extracting specific descriptive text or data fragments from the corresponding dimension requirements of the dynamic job competency model and the corresponding dimension evaluation results of the dynamic talent profile based on the key dimensions determined by the attention weight map; and generating a natural language description of the matching interpretation statement based on a predefined interpretation template, wherein the key dimensions, the dimension requirements, the evaluation results, and the sources on which the evaluation results were generated are associated and combined.

[0078] It should be further explained that generating natural language matching and interpretation statements is an automated process from structured data to readable text, and the specific steps are as follows: (1) Key dimension pair identification: Receive attention weight matrix from matching calculation module And dimensional importance weights First, identify the importance weights. The top K job dimensions are denoted as a list. For each high-weight job dimension Find the weight values ​​in the i-th row of the attention matrix. The highest talent dimension This creates K key dimension pairs: The optimal value for K is 3, because it can generate interpretations with sufficient information without causing information overload.

[0079] (2) Specific information extraction: For each key dimension : (a) Extracting job dimension requirements: From the raw data of the dynamic job competency model, find the requirements that match the job competency requirements. The specific description corresponding to the dimension. For example, if If the skill is "Java skills," then extract the original text snippets related to Java requirements from the job description, such as "Proficient in Java programming, familiar with multithreading and JVM tuning." If this dimension is supplemented through knowledge graph associations, then extract the standard description of the skill from the knowledge graph, such as "Java is a widely used high-level programming language."

[0080] (b) Extracting talent assessment results and evidence: From the raw data of the dynamic talent profile, find relevant data... The corresponding evaluation values ​​and evidence for each dimension.

[0081] ①If For hard skills (such as "Java skills"), the evaluation value might be the skill proficiency level (such as "proficient") derived from the resume. Evidence would be specific descriptive snippets in the resume, such as "Developed a high-concurrency order system using Java in Project X".

[0082] ②If For soft skills dimensions (such as "logical thinking"), the evaluation value is the score given by the AI ​​interview (e.g., 0.85). Evidence requires further linking to the specific records of the AI ​​interview. The system will trace back to the specific question and answer that generated the score. For example, the logical thinking score is primarily based on the answer to the question, "Please describe the most complex technical problem you have ever solved." The system will extract the text of the question (or the question ID) and fragments of the candidate's answer (e.g., "First, I located the database connection pool exhaustion through logs; then, I analyzed the code segments for connection request and release..."). Additionally, if there are multimodal analysis conclusions, they will be added, such as, "When answering this question, the candidate's speech rhythm was steady, and the expression was clear and well-structured."

[0083] (3) Template Selection and Filling: The system maintains an extensible interpretation template library. Templates are sentences with placeholders. The system selects a template based on the matching level (high match, medium match, low match) and dimension type (skill match, competency match). For example: a high match template (when...) (At the time): "In terms of [job dimension], the job requirements are described in '[job requirement description]'. The candidate demonstrates strong [talent dimension] abilities (score: [assessment score]), specifically reflected in [source of evidence]." Medium match template (when...) (At the time): "In terms of [Job Dimension], the job requirements are described in the job description. The candidate possesses certain [Talent Dimension] abilities (score: [Assessment Score]), such as [Source of Evidence]. However, there may be room for improvement in [Specific Weaknesses]." Weaknesses can be automatically inferred or pre-defined by comparing the differences between the job requirement description and the candidate's evidence. Then, the extracted information is used to populate the template. For example, the populated template might say: "In terms of 'Java Programming Skills,' the job requirements are 'Proficient in Java programming, familiar with multithreading and JVM tuning.' The candidate demonstrates strong 'Java project experience' (score: 0.92), specifically reflected in their resume description of 'Developing a high-concurrency order system using Java in Project X and performing JVM tuning to improve performance by 20%'." (4) Sentence polishing and combination: The sentences generated by the K key dimensions are sorted according to their weights. Arrange the sentences in descending order of match score. Add conjunctions between sentences, such as "firstly," "secondly," and "in addition." Finally, combine them into a coherent, paragraph-style match score interpretation text, which is presented to the user next to the match score.

[0084] Understandably, this template-based and structured data-filling interpretation generation method achieves efficient and accurate conversion between machine-understandable data and human-readable text. Its beneficial effects are multifaceted: First, it provides high interpretability, transforming abstract matching scores into concrete and intuitive textual descriptions, enabling HR to quickly understand the basis for AI recommendations and enhancing the system's credibility and usability. Second, the interpretation content is traceable because the cited evidence (such as resume excerpts and interview questions) is directly linked to the original data, allowing HR to click to view details for verification or in-depth evaluation. Third, this interpretation itself provides a valuable entry point for subsequent interviews. HR can design more targeted interview questions based on the candidate's strengths or potential weaknesses mentioned in the interpretation, seamlessly connecting AI screening with human interviews, greatly improving the efficiency and depth of the overall recruitment process. This transforms AI from a black-box screening tool into an intelligent recruitment assistant.

[0085] Specifically, the optimization based on the enterprise's subsequent operational data regarding the recommendation results and the candidate's performance feedback data after onboarding is carried out through a continuous learning mechanism. This includes: collecting the enterprise's full-process operational logs for screening, interviewing, and hiring decisions of recommended candidates, as well as anonymized periodic performance evaluation data of candidates after onboarding; using the operational logs and performance feedback data as environmental feedback signals for reinforcement learning to construct a reward function aimed at improving the long-term talent matching success rate; and iteratively updating the feature extraction network parameters used for feature extraction in the dynamic capability assessment model and the neural network parameters used for calculating weights and similarities in the deep matching algorithm through a policy gradient method, thereby achieving self-iterative optimization of the assessment and matching model.

[0086] It should be further explained that the continuous learning mechanism achieves automatic optimization of model parameters through an online reinforcement learning framework. The specific implementation process is as follows: (1) Data Collection and Status Representation: The system records each recommendation list (e.g., TOP 10 candidates) issued for a specific position. For each candidate in the list, a series of HR actions and timestamps are recorded: View resume (Yes / No) Initiate an interview (Yes / No) Interview rating (if any) Issue an offer (yes / no) Candidate accepts offer (yes / no) Candidate onboarding (yes / no). If the candidate is onboarded, their anonymized performance evaluation data will be collected subsequently (e.g., after 3 months, 6 months). (For example, mapping "S / A / B / C" performance ratings to numerical values ​​1.0 / 0.8 / 0.6 / 0.4). All this data constitutes a trajectory sequence. The state of the system. The parameters at time t are defined as the current model parameters. This refers to a certain representation of the data, as well as the statistical characteristics of a recent batch of recommendation results, such as the average matching degree of all recommendations in the past week, and the average action rate of HR on recommendations (view rate, interview rate, etc.). For simplicity, the status is often... Approximate to the current model parameters itself.

[0087] (2) Definition of action space: the actions of the system For model parameters This provides a suggestion for the direction of an update (i.e., the gradient). More specifically, the action space can be high-dimensional, with parameters... Same dimension. Action It can be a multidimensional vector, indicating the pair Each parameter in the algorithm is either subjected to a small positive perturbation, a negative perturbation, or remains unchanged. In practical policy gradient algorithms, actions are typically determined by the policy network. The probability distribution is sampled from the output, and the average of this distribution can be considered as a suggested update direction. .

[0088] (3) Reward function design: reward function The design goal is to guide the model's learning, making its recommended candidates more likely to be adopted by HR and ultimately achieve high performance. Rewards are delayed and sparse. We design the rewards for a complete "referral-onboarding-performance" cycle as a weighted sum of rewards across multiple stages: in: Participation Rewards This reflects the HR's initial interest in the recommendation results. For example, each candidate whose resume is viewed by HR receives a reward of +0.1; each candidate whose interview is initiated receives a reward of +0.3. This encourages the model to recommend eye-catching candidates.

[0089] Conversion Rewards This reflects the successful progress of the recruitment process. For example, a bonus of +0.5 is awarded for each candidate who receives an offer; and +1.0 is awarded for each candidate who is ultimately hired. This encourages the model to recommend candidates who are not only viewed but also ultimately hired.

[0090] Performance bonus This reflects long-term success. Upon joining the company, an employee's normalized performance score is used during their initial performance review (e.g., 6 months later). (Range 0-1) Multiplied by a coefficient as a reward, for example The rationale for a coefficient of 2.0 is that performance is the gold standard for measuring the success of a match and should be given the highest weight, making it significantly greater than process rewards.

[0091] Loss penalty If an employee leaves within a short period (such as during probation), a large negative bonus is given, such as -1.5, because early departure usually signifies a severe mismatch. Weighting The preferred values ​​for these weights can be set to 0.5, 1.0, 1.5, and 1.2. These weights need to be calibrated through A / B testing or simulations based on historical data to ensure that the model focuses on final performance output and stability while paying attention to process efficiency.

[0092] Policy optimization and parameter update: The Proximal Policy Optimization (PPO) algorithm is used to update the policy network. parameters The actions output by the policy network are then applied to update the parameters of the main model. The PPO algorithm ensures stability by limiting the magnitude of each policy update. Its objective function is: in, It is the probability ratio of the new strategy to the old strategy. It is the estimated advantage function, representing the advantage function in state Take action below How much better it is than the average situation is usually calculated using the generalized advantage estimate (GAE). This is a hyperparameter, with an optimal value of 0.2, used to limit the update step size. By maximizing this objective function, the policy network learns to generate actions that yield higher cumulative rewards (i.e., the direction of model parameter updates). Main model parameters Then update according to the direction suggested by the policy network: ,in This is the learning rate of the main model. In this way, the parameters of the dynamic capability assessment model and the deep matching algorithm can be continuously optimized to better predict and meet the talent preferences and performance standards of enterprises.

[0093] Understandably, this reinforcement learning-based continuous learning mechanism transforms the recruitment system from a static auxiliary tool into an intelligent agent capable of learning and evolving from actual recruitment results. Its core benefit lies in making the system "smarter with use." The system no longer relies solely on initial training data but gains real-time, high-quality feedback signals from actual corporate recruitment behaviors (HR screening, interviews, and hiring decisions) and the actual work outcomes (performance) of candidates. Through the design of the reward function, the system is guided to learn the characteristic patterns of candidates who not only pass resume and interview screening but also demonstrate actual job competence, high performance, and stability. In the long run, the system can adapt to the unique cultures and success standards of different companies, automatically discovering those "implicit" success factors that are difficult to describe by rules, thereby continuously improving the accuracy and long-term value of talent recommendations and fundamentally optimizing the return on investment in recruitment for enterprises.

[0094] Specifically, the continuous learning mechanism is implemented using a federated learning framework, including: on a client deployed locally within the enterprise, the local model is trained using the enterprise's anonymized follow-up operation data and performance feedback data to generate model parameter update gradients; the model parameter update gradients are encrypted and uploaded to a central server; the central server aggregates encrypted gradients from multiple enterprises, updates global model parameters using a secure aggregation algorithm, and distributes the updated global model parameters to each client; each client uses the global model parameters to update its local model, thereby achieving the goal of jointly optimizing the performance of the global recruitment matching model using cross-enterprise data while protecting the data privacy of each enterprise.

[0095] It should be further explained that the specific deployment and training protocols for the federated learning framework are as follows: 1. System Initialization: The central server (cloud service) initializes a global recruitment matching model, denoted as... Its parameters are The model includes parameters for all trainable modules, such as dynamic profile building and interpretable matching. Each participating enterprise (customer) deploys a client containing the local model in its on-premises data center or private cloud. Its initial parameters are the same as those of the global model: Let enterprise i's local private dataset be denoted as... This includes its historical recruitment logs, operation records, and anonymized performance data of newly hired employees. Never leave the company's local environment.

[0096] 2. Federated Learning Training Rounds (Round t): (a) Server broadcast: The central server broadcasts the current global model parameters. After encryption (e.g., using homomorphic encryption public keys), the data is broadcast to all clients participating in this round of training. To reduce communication costs, only some key parameters can be sent, or model differential compression techniques can be used.

[0097] (b) Local client update (in parallel): ① Client i downloads global parameters and uses them to update the local model: .

[0098] ② Client i in its own private dataset Local training is performed. The training objective can be supervised learning (e.g., using historical admission decisions as labels) or reinforcement learning-based (as described in the preceding claim). Local training is performed for multiple epochs (E, preferably 3-5 to prevent overfitting to local data). Optimization is performed using stochastic gradient descent (SGD) or its variants (e.g., Adam). After local training is complete, updated local parameters are obtained. .

[0099] ③ Client i calculates the local model parameter update gradient (i.e., local update amount): .

[0100] ④ Privacy protection processing: Client i updates the gradient locally The process involves two steps. First, gradient clipping is performed, restricting each dimension of the gradient vector to the range [-C, C], where C is the clipping threshold, preferably 0.1, to control the potential impact of a single client on the global model and enhance stability. Second, differential privacy (DP) noise is added. The client generates a random noise vector that follows a Gaussian distribution. ,in It is a noise multiplier. It is the identity matrix. Then, the noisy gradient update is sent to the server: Noise multiplier The choice depends on the required privacy budget. For example, in hour, It can provide strong differential privacy protection. This ensures that information about the original local data cannot be inferred from the noisy gradient update.

[0101] (c) Secure aggregation: Each client will update its local data with added noise. The encrypted upload is sent to the central server. The server uses the Secure Aggregation Protocol. This protocol allows the server to calculate the sum of all client updates without decrypting individual client updates. A classic approach is to utilize secret sharing and homomorphic encryption. Ultimately, the server obtains the aggregated global update gradient: , where N is the number of clients participating in this round of training.

[0102] (d) Global model update: The server updates the global model using the aggregated gradients. .in, This is the global learning rate, which is typically set to 1.0 or a value less than 1.0 for smooth updates.

[0103] (e) Model distribution: The server will distribute the updated global model parameters. Distribute back to all clients (or only clients that participated in this round of training).

[0104] 3. Iteration and convergence: Repeat step 2 to perform multiple rounds of federated learning training until the global model's performance on the validation set (which can be achieved by the server holding a small amount of public data or by the client evaluating locally and then uploading the evaluation metrics) converges, or the preset number of training rounds T is reached (preferably 50-100 rounds).

[0105] Understandably, adopting a federated learning framework for continuous learning offers revolutionary benefits. First, it completely resolves concerns about enterprise data privacy and security. Enterprise recruitment and employee performance data are highly sensitive core assets. Traditional centralized training requires data to be uploaded to the cloud, posing a risk of leakage. The federated learning model keeps data permanently on-premises, uploading only encrypted, noisy model update gradients, thus guaranteeing data sovereignty and encouraging collaboration from numerous enterprises (especially those in sectors with stringent data security requirements such as finance and healthcare). Second, it aggregates diverse recruitment feedback data from numerous enterprises, training a more generalized, robust, and fair global model. While individual enterprises have limited data and may contain biases, cross-industry and cross-scale enterprise data aggregation allows the model to learn more universally effective talent matching patterns, reducing bias towards specific groups (such as specific schools or backgrounds). Finally, each enterprise ultimately obtains a powerful foundational model trained on massive amounts of diverse data. Enterprises can then further refine this model using their local data, resulting in an AI recruitment system that possesses general intelligence while tailored to their specific needs. This enables the sharing of data value while strictly protecting privacy, and promotes the intelligent upgrade of the entire recruitment ecosystem.

[0106] This invention provides another embodiment, which offers an AI-powered intelligent recruitment matching and recommendation system based on dynamic profiles. The AI-powered intelligent recruitment matching and recommendation system based on dynamic profiles includes: ① The dynamic profile building module is used to perform the steps of building a dynamic job competency model and a dynamic talent profile.

[0107] It should be further explained that the dynamic profile building module consists of multiple sub-services, the specific design of which includes: (1) Job Parsing Service: A resident microservice that loads a pre-trained BERTNER model and a relation extraction model. It receives job description text via a RESTful API and returns a structured list of entities such as skills and experience. It calls a knowledge graph query service to obtain related information.

[0108] (2) Knowledge graph service: Based on graph databases (such as Neo4j), it stores entities and relationships such as industry skills, positions, and courses, and provides efficient graph traversal and query interfaces.

[0109] (3) Resume parsing service: It has deployed an OCR engine and a resume-specific information extraction model, which can process resumes in various formats such as PDF, Word, and images, and output standardized JSON resume data.

[0110] (4) AI Interview Engine Service: A highly available distributed service that includes question management, real-time audio and video processing, ASR transcription, and multiple modal AI analysis models (text NLP model, speech emotion model, computer vision model). It interacts with the candidate's front end via WebSocket, processes audio and video streams in real time, and calls the backend model service for analysis, ultimately generating a dynamic competency assessment report.

[0111] (5) Profile Fusion Service: Responsible for receiving data from job analysis, resume analysis and AI interview engine, cleaning, aligning, quantifying and vectorizing the data according to the preset unified dimensional framework (such as 8-dimensional capability model), and finally generating a standard format dynamic job capability model and dynamic talent profile object, which are stored in the central profile database.

[0112] ② The intelligent matching and recommendation module is used to perform the steps of calculating the matching degree and generating recommendation results.

[0113] It should be further explained that the core of the intelligent matching and recommendation module is an interpretable deep matching model service, the specific design of which includes: (1) Matching Calculation Service: Loads a pre-trained deep matching neural network model (e.g., deployed based on PyTorch or TensorFlow Serving). This service receives a pair of job profile IDs and candidate profile IDs, reads the corresponding vectors from the profile database, inputs them into the model for calculation, and returns intermediate results such as matching score and attention weight matrix.

[0114] (2) Interpretation and generation service: Based on the attention weight returned by the matching calculation service, extract the corresponding original text description and evidence fragments from the profile database, call the rule engine or lightweight text generation model, fill the predefined template, and generate natural language interpretation.

[0115] (3) Recommendation sorting service: For a given position, retrieve all candidates from the candidate pool, call the matching calculation service to calculate the matching degree in batches, sort them from high to low according to the score, generate a recommendation list, and encapsulate the list and interpretation together and return it to the front-end application.

[0116] ③ The continuous learning optimization module is used to execute the steps of optimizing model parameters through a continuous learning mechanism.

[0117] It should be further noted that the continuous learning optimization module is implemented using a federated learning architecture, and its specific design includes: (1) Local client agent: Deployed in the private environment of each enterprise. It contains a local model copy, a local dataset cache, and federated learning client logic. The client agent starts the local training task regularly (e.g., every early morning), uses local anonymized operations and performance data, trains the local model based on reinforcement learning algorithms such as PPO, calculates gradient updates, performs differential privacy noise addition, and uploads the encrypted gradient updates to the central federated learning server through a secure HTTPS channel.

[0118] (2) Federated Learning Aggregator Server: Deployed in the cloud and maintained by the system provider. It is responsible for coordinating the federated learning training rounds, receiving encrypted updates uploaded by each client, running the secure aggregation protocol, calculating global gradient updates, and updating the global model. The updated global model parameters are encrypted and sent to each client.

[0119] (3) Model Version Management and Release System: Manages different versions of the global model, supporting A / B testing and canary releases. Once a new global model is validated, the system coordinates each client agent to securely update its local model, ensuring a smooth service upgrade. All these modules communicate asynchronously via an Enterprise Service Bus (ESB) or message queues (such as Kafka) to ensure high availability and scalability of the system. The front-end interactive interfaces (HR workbench and candidate interview interface) interact with these back-end microservices through an API gateway.

[0120] Understandably, the system's modular and microservice design gives it high scalability, maintainability, and flexibility. Each module can be developed, deployed, and upgraded independently. For example, when a more advanced NLP model emerges, the job parsing service can be upgraded separately without affecting other modules. The introduction of the federated learning architecture allows the continuous learning and optimization module to pool the wisdom of numerous enterprises to jointly train a powerful global model, while strictly adhering to data privacy regulations, and simultaneously allowing each enterprise to perform personalized fine-tuning locally. This is equivalent to providing each enterprise with a dedicated AI recruitment expert that "understands you better the more you use it" and "draws on the strengths of many." The entire system, from data collection, intelligent processing, result push to feedback learning, forms an efficient and automated closed loop, continuously improving the accuracy and efficiency of recruitment and providing a powerful technological engine for enterprise talent acquisition.

[0121] In a preferred embodiment, this application also provides an electronic device, the electronic device comprising: The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the AI-based intelligent recruitment matching and recommendation method based on dynamic profiles. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0122] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0123] Those skilled in the art will understand that the method steps of this invention can be performed by a computer program instructing related hardware, such as a computer device or processor, which may be stored in a non-transitory computer-readable storage medium. When the computer program is executed, the steps of this invention are performed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0124] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0125] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. An AI-powered intelligent recruitment matching and recommendation method based on dynamic profiles, characterized in that, include: S1. Construct a dynamic job competency model for the enterprise. The dynamic job competency model is a structured model generated based on job description text and associated with an industry skill standard knowledge graph. S2. Construct a dynamic talent profile of the candidate, which is generated by integrating the candidate's static resume information with the dynamic ability assessment results output from the AI ​​interview system. S3. Based on an interpretable deep matching algorithm, calculate the matching degree between the dynamic job competency model and the dynamic talent profile in the multi-dimensional vector space, and generate recommendation results containing matching degree interpretation information; S4. Based on the enterprise's subsequent operational data regarding the recommendation results and the candidate's performance feedback data after joining the company, optimize the parameters of the dynamic capability assessment model and the deep matching algorithm through a continuous learning mechanism.

2. The method according to claim 1, characterized in that, The construction of the enterprise's dynamic job competency model includes: The job description text is structured and parsed to extract key elements such as core responsibilities, essential skills, and expected traits. From the industry skill standard knowledge graph, standard skill nodes, skill hierarchy relationships, and logical dependencies between skills associated with the key elements are obtained. Based on the key elements and their associated graph information, a structured capability framework including skill dimension, experience dimension, potential dimension, and team culture adaptation dimension is generated as the initial dynamic job capability model.

3. The method according to claim 2, characterized in that, The knowledge graph of related industry skill standards also includes: Based on the company's industry, size, and historical recruitment data, the relevant industry-wide general skill standards and company-specific skill preferences are dynamically matched and weighted from the knowledge graph. The company-specific skill preferences are obtained by profiling and analyzing the group of successful candidates recruited by the company's team in the past, so that the generated dynamic job competency model has both industry universality and company specificity.

4. The method according to claim 1, characterized in that, The construction of dynamic talent profiles for candidates includes: The process involves analyzing a candidate's static resume information to extract structured data related to their educational background, work experience, project experience, and skill certificates; obtaining dynamic competency assessment results output by an integrated AI interview system, which include at least scores for logical thinking and communication skills derived from interview question and answer text analysis, and scores for resilience and teamwork tendency derived from multimodal analysis of interview videos; and aligning and integrating the structured data with the dynamic competency assessment results, mapping them to a unified assessment dimension system to form the dynamic talent profile.

5. The method according to claim 4, characterized in that, The generation of the dynamic capability assessment results also includes: During the AI ​​interview process, the system records and analyzes in real time the candidate's answers to preset scenario questions and behavioral interview questions, as well as changes in voice tone and facial micro-expressions. Using pre-trained natural language processing and computer vision models, feature vectors related to soft skills are extracted from text, speech, and visual modalities, respectively. The feature vectors are integrated and analyzed through a multimodal fusion network to quantify and output the candidate's evaluation scores on multiple soft skill dimensions, serving as an important supplement to the hard skill information in the static resume.

6. The method according to claim 1, characterized in that, The calculation of the matching degree based on the interpretable deep matching algorithm includes: The dynamic job competency model and the dynamic talent profile are encoded into feature vectors with the same dimension. The relevance weights of the two feature vectors in each dimension are calculated through an attention mechanism network, and the overall matching score is calculated based on the weighted vector similarity. At the same time, the visual attention weight map generated by the attention mechanism network is used to identify the key dimensions that contribute the most to the overall matching score, which serves as the basis for interpreting the matching score.

7. The method according to claim 6, characterized in that, Generating the matching degree interpretation information further includes: Based on the key dimensions determined by the attention weight map, specific descriptive text or data fragments are extracted from the corresponding dimension requirements of the dynamic job competency model and the corresponding dimension evaluation results of the dynamic talent profile. Based on a predefined interpretation template, the key dimensions, the dimension requirements, the evaluation results, and the sources from which the evaluation results are generated are correlated and combined to generate matching interpretation statements in natural language. The sources from which the interpretations are generated include, but are not limited to, specific questions in AI interviews, fragments of candidates' answers, or conclusions from multimodal analysis.

8. The method according to claim 1, characterized in that, The optimization, based on the company's subsequent operational data regarding the recommendation results and the candidate's performance feedback data after joining the company, is carried out through a continuous learning mechanism, including: Collect the entire process logs of candidate screening, interviewing, and hiring decisions from the enterprise side, as well as the anonymized periodic performance evaluation data of candidates after they have joined the company; use the operation logs and performance feedback data as environmental feedback signals for reinforcement learning to construct a reward function aimed at improving the long-term talent matching success rate; and iteratively update the feature extraction network parameters used to extract features in the dynamic ability assessment model and the neural network parameters used to calculate weights and similarities in the deep matching algorithm through the policy gradient method to achieve self-iterative optimization of the assessment and matching model.

9. The method according to claim 8, characterized in that, The continuous learning mechanism is implemented using a federated learning framework, including: On the client deployed locally within the enterprise, the local model is trained using the enterprise's anonymized post-operation data and performance feedback data to generate model parameter update gradients. These gradients are then encrypted and uploaded to a central server. The central server aggregates encrypted gradients from multiple enterprises, updates the global model parameters using a secure aggregation algorithm, and distributes the updated global model parameters to each client. Each client uses these global model parameters to update its local model, thereby enabling the optimization of the global recruitment matching model's performance using cross-enterprise data while protecting the data privacy of each enterprise.

10. An AI-powered intelligent recruitment matching and recommendation system based on dynamic profiles, characterized in that: The AI-powered intelligent recruitment matching and recommendation method based on dynamic profiles as described in any one of claims 1-9 includes: The dynamic profile building module is used to perform the steps of building a dynamic job competency model and a dynamic talent profile. The intelligent matching and recommendation module is used to perform the steps of calculating the matching degree and generating recommendation results; The continuous learning optimization module is used to perform the steps described above for optimizing model parameters through a continuous learning mechanism.

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