Flexible labor two-way matching method based on multi-dimensional image and real-time demand

Abstract

The application provides a flexible labor two-way matching method based on multi-dimensional images and real-time demand, constructs high-dimensional skill semantic features through resume and skill data cleaning, skill label normalization and vectorization, fuses semantic similarity and skill co-occurrence frequency with industry recruitment data to generate skill transfer strength scores and establish a heterogeneous transfer network, identifies the required associated skill path of the post through graph diffusion algorithm, realizes labor cross-field skill evidence mining and causal sensitivity analysis, uses a templated natural language generator to output structured recommendation reasons and realizes online optimization driven by user interactive feedback, and can quantify the skill transfer potential of the labor, improve the cross-industry matching ability, and significantly enhance the explainability and reliability of the post recommendation results.

Description

Technical Field

[0001] This invention relates to the field of intelligent recruitment recommendation and skills semantic modeling technology, and in particular to a flexible two-way matching method for employment based on multi-dimensional profiles and real-time needs. Background Technology

[0002] With the rapid development of flexible employment platforms and intelligent job matching systems, job demand recommendation based on candidate skill profiles has become an important direction for human resource technology innovation. Current mainstream technologies typically employ multi-dimensional label mapping, semantic embedding, and knowledge graph structures, using deep learning or graph neural networks to achieve automated adaptation between worker skills and job requirements. In recent years, skill semantic modeling (such as pre-trained occupational models like JobBERT), skill relationship mining based on co-occurrence statistics, and weakly supervised algorithms such as graph diffusion have gradually become widespread in the field of job matching and recommendation, forming a technical roadmap centered on skill vectorization, job requirement semanticization, and graph structuring. These technologies effectively improve the efficiency and breadth of matching and are applied to flexible employment platforms with high-frequency changes and diverse job requirements. Typical job matching and recommendation systems focus on modeling the direct correlation between skills and job requirements within a single domain. For example, the system often captures the direct skill overlap between a candidate's resume and the target job requirements, or uses attention mechanisms to focus on key tags to improve matching accuracy. Some technologies introduce interpretability mechanisms, such as knowledge graph visualization and attention weight display, to help users understand the recommendation results. However, in cross-industry and cross-domain skill transfer scenarios, current technologies mostly rely on hard label mapping within known domains or global knowledge links, lacking the exploration of skill transfer paths and their intensity, and failing to organically combine correlation and causality, resulting in one-sided explanatory logic and an inability to cover complex career transition paths. With the increasing sophistication of workers' skills and accelerated career mobility, the demand for cross-domain matching in flexible employment scenarios is becoming increasingly prominent. Workers often possess the potential for cross-industry skill transfer; for example, data analysis skills can be transferred to various positions such as marketing and operations. However, current cross-domain skill matching recommendation systems have significant shortcomings in interpretability. First, the systems can only output "similar skills" or simple co-occurrence evidence, failing to present transfer paths and causal effects. Second, mainstream interpretability technologies rely on the disclosure of the global structure of knowledge graphs or the visualization of the attention distribution of complex deep models, which, limited by data plagiarism risks and privacy compliance, lack the ability to generate interactive recommendation reasons. Third, existing systems mostly use static templates for explanation, failing to dynamically generate personalized recommendation evidence based on specific skill transfer paths and job scenarios, making it difficult to answer core questions such as "why recommend," "how to transfer," and "the weight of skill influence." Summary of the Invention

[0003] In order to solve the above-mentioned technical problems, this invention provides a flexible two-way matching method for employment based on multi-dimensional profiles and real-time needs.

[0004] The technical solution of this invention is implemented as follows: a flexible two-way matching method for employment based on multi-dimensional profiling and real-time needs, comprising: S1: Obtain workers' historical work experience, project experience, and skill tag data, and vectorize each skill based on a pre-trained occupational semantic model to generate a high-dimensional semantic vector representation of the skill; S2: Based on the high-dimensional semantic vector representation of the skills and the industry co-occurrence statistics in the standardized skill library, calculate the semantic similarity between skills and the cross-job co-occurrence frequency, and construct a skill semantic transfer graph, where the nodes are standardized skill items and the edge weights reflect the potential transfer intensity between skills. S3: Input the core skill requirements of the target job into the skill semantic transfer graph, execute the graph diffusion algorithm to identify cross-domain related skill subgraphs connected to the core skills, and generate an extended matching candidate set containing potential transferable skill paths; S4: Compare the high-dimensional semantic vector representation of the worker's skills with the extended matching candidate set to locate cross-domain skill nodes that the worker possesses but are not directly related to, and generate a cross-domain skill matching evidence set; S5: Based on the cross-domain skill matching evidence set, the counterfactual perturbation analysis method is used to perform virtual removal operations on each dimension of the worker's skill vector in turn, calculate the change in the matching score after each perturbation, identify the key supporting skills that have a significant impact on the matching results, and form a causal sensitivity feature sequence. S6: Input the causal sensitivity feature sequence, skill transfer intensity score and job requirement keywords into the template-guided natural language generator to generate a set of structured explanatory elements and output human-readable recommendation reason text; S7: Bind the recommendation reason text with the corresponding job matching result to form a recommendation item with a dynamic and interpretable path, and provide an interactive expansion mechanism in the user interface to display the matching logic chain; S8: Monitor user feedback on the adoption behavior of recommended items with dynamic interpretable paths, update the edge weight parameters in the skill semantic transfer graph and the template weights of the natural language generator based on the feedback results, and realize online optimization of the interpretable recommendation model.

[0005] The flexible two-way matching method for employment based on multi-dimensional profiling and real-time needs provided by this invention has the following beneficial effects: (1) This invention achieves effective identification of skills that are not directly related to workers but have potential transfer value by constructing a lightweight skill semantic transfer graph and combining it with a graph diffusion algorithm. By using a pre-trained occupational semantic model to map skills into high-dimensional vectors, and jointly modeling graph structure edge weights based on semantic similarity and industry co-occurrence frequency, it not only enhances the ability to express implicit associations between skills, but also effectively supports the mining of career opportunities across industries and functions. Especially in the context of flexible employment, facing the characteristics of high-frequency updates in job requirements and dynamic changes in skill combinations, it can quickly generate personalized recommendations, significantly improving the relevance and diversity synergy of recommendations; (2) This invention introduces a counterfactual reasoning mechanism and a template-guided natural language generation module to construct a dynamic explanation system with causal sensitivity analysis capabilities. Unlike traditional attention weight visualization or rule matching explanation methods, this solution quantifies the impact of each skill on the final matching score by perturbing specific dimensions in the worker's skill vector, thereby identifying the core skill nodes that constitute key supporting evidence and incorporating them into the explainable path. This process enables the system not only to answer "what was recommended" but also to clearly explain "why it was recommended," for example, explicitly stating that "although data analysis experience is not directly required for the target position, it has strong transferability in statistical modeling ability." The generated explanation text integrates job keywords, skill transfer intensity scores, and sensitivity analysis results, with natural language and rigorous logic, significantly improving the trust and transparency of human-computer interaction. At the same time, the entire reasoning and generation process is lightweight and efficient, requiring no complex parameter tuning or large-scale model deployment, and can complete matching and explanation output within millisecond response time, fully meeting the dual requirements of real-time performance and scalability of flexible employment platforms; (3) This invention constructs a closed-loop adaptive intelligent recommendation architecture by combining semantic graph structure modeling with causal analysis. This not only improves the system's decision-making quality and user experience but also has good maintainability and scenario extensibility. Furthermore, it avoids dependence on large-scale labeled data and complex ontology alignment, reducing system construction and maintenance costs. Simultaneously, the constrained multi-hop graph diffusion strategy effectively controls computational complexity and prevents noise interference caused by excessive information propagation. In addition, the system supports interactive unfolding of the recommendation logic chain, enabling on-demand presentation of explanatory content while balancing simplicity and in-depth insight. Attached Figure Description

[0006] Figure 1 The flowchart shows the flexible two-way matching method for employment based on multi-dimensional profiling and real-time needs according to the present invention. Figure 2 This is a sub-flowchart of the flexible two-way matching method for employment based on multi-dimensional profiling and real-time needs according to the present invention. Figure 3This is another sub-flowchart of the flexible two-way matching method for employment based on multi-dimensional profiling and real-time needs according to the present invention. Detailed Implementation

[0007] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0008] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0009] like Figure 1 As shown, this invention provides a flexible two-way matching method for employment based on multi-dimensional profiles and real-time needs, specifically including: S1: Obtain workers' historical work experience, project experience, and skill tag data, and vectorize each skill based on a pre-trained occupational semantic model to generate a high-dimensional semantic vector representation of the skill; S2: Based on the high-dimensional semantic vector representation of the skills and the industry co-occurrence statistics in the standardized skill library, calculate the semantic similarity between skills and the cross-job co-occurrence frequency, and construct a skill semantic transfer graph, where the nodes are standardized skill items and the edge weights reflect the potential transfer intensity between skills. S3: Input the core skill requirements of the target job into the skill semantic transfer graph, execute the graph diffusion algorithm to identify cross-domain related skill subgraphs connected to the core skills, and generate an extended matching candidate set containing potential transferable skill paths; S4: Compare the high-dimensional semantic vector representation of the worker's skills with the extended matching candidate set to locate cross-domain skill nodes that the worker possesses but are not directly related to, and generate a cross-domain skill matching evidence set; S5: Based on the cross-domain skill matching evidence set, the counterfactual perturbation analysis method is used to perform virtual removal operations on each dimension of the worker's skill vector in turn, calculate the change in the matching score after each perturbation, identify the key supporting skills that have a significant impact on the matching results, and form a causal sensitivity feature sequence. S6: Input the causal sensitivity feature sequence, skill transfer intensity score and job requirement keywords into the template-guided natural language generator to generate a set of structured explanatory elements and output human-readable recommendation reason text; S7: Bind the recommendation reason text with the corresponding job matching result to form a recommendation item with a dynamic and interpretable path, and provide an interactive expansion mechanism in the user interface to display the matching logic chain; S8: Monitor user feedback on the adoption behavior of recommended items with dynamic interpretable paths, update the edge weight parameters in the skill semantic transfer graph and the template weights of the natural language generator based on the feedback results, and realize online optimization of the interpretable recommendation model.

[0010] Step S1: Obtain the worker's historical work experience, project experience, and skill tag data, and vectorize each skill based on a pre-trained occupational semantic model to generate a high-dimensional semantic vector representation of the skill. Specifically, this includes: S1.1: Obtain the structured and unstructured resume data of workers as raw text data, wherein the structured resume data includes explicitly labeled skill tags, and the unstructured resume data includes historical work experience text and project description information; clean the raw text data to remove irrelevant characters and standardized terminology to obtain standardized professional background input information. The input objects for obtaining structured and unstructured resume data of workers are raw datasets that include text of historical work experience, descriptions of projects participated in, and explicitly labeled skill tags, covering multiple sources such as recruitment platform databases, enterprise human resource management systems, and third-party resume parsing interfaces; By using the data source interface call method (parameters: API authentication token, batch retrieval pagination configuration), the function of batch retrieval of resume data from various data sources is realized, and the raw multi-format text collection is obtained. Furthermore, by using a unified encoding conversion method (parameters: UTF-8 standard encoding, illegal character filtering rules), the encoding of the original text set is integrated, eliminating parsing anomalies caused by inconsistent character sets, and generating text stream data results with unified encoding. Furthermore, by using a noise filtering algorithm based on regular expressions (parameters: specific non-linguistic character matching pattern, interval position truncation rule), redundant symbols, HTML tags and special control characters in the text stream data are removed, and a set of plain text professional resumes after noise filtering is obtained. Further, by using a professional term standardization method (parameters: industry term replacement dictionary, job title mapping table), the unified expression of occupational terms in the text set is realized, the phenomena of synonymous different writings, abbreviations and mixed use of multiple languages are replaced, and occupational background text data with standardized terms is generated; Through text cleaning and standardization processing, the result of the previous step is converted into standardized occupational background input information, so as to provide a high-consistency and high-quality data basis for subsequent skill extraction and vectorization modeling; Exemplarily, in a cross-domain job matching task, the worker resume data comes from the export file of the enterprise HR system and the API query result of the recruitment platform. The paging size is set to 500 entries through the interface calling method, each request returns a unified JSON object array, and a total of 20,000 resume records are captured. In the encoding conversion stage, all text streams are converted to UTF-8, and those not in the common Chinese character range are excluded. The noise filtering algorithm uses regular pattern matching to remove HTML tags and advertising watermarks. For term standardization, an industry dictionary containing 5000 mapping rules is used to unify "DBA" and "Database Administrator" into "Database Administrator", and unify "SW Eng" and "Software Engineer" into "Software Engineer". The cleaned standardized text set retains an average of about 350 valid Chinese characters and English terms per resume, which serves as the input of the downstream skill extraction module, ensuring that the matching accuracy of skill words is significantly improved during vectorization representation; S1.2: Based on the standardized occupational background input information, extract the skill-related segments contained therein, use regular matching and keyword recognition technology to extract candidate skill items from the text, and perform fuzzy alignment with the entries in the standardized skill library to generate a normalized skill label set; Based on the standardized occupational background input information, a regular matching method (parameters: predefined skill pattern set, case-insensitive flag) is adopted to realize the preliminary identification of segments that conform to the skill semantic pattern in the text data; Further, through a keyword recognition algorithm (parameters: skill keyword dictionary, word segmentation granularity setting), non-explicit skill descriptions are converted into indexable skill phrases, and a list of candidate skill items is obtained; Further, through a fuzzy string matching algorithm (parameters: Levenshtein distance threshold, similarity weighting coefficient), the corresponding matching between candidate skill items and standardized skill library entries is realized, and a similarity score for each pair of matches is generated; Further, through a weighted rule mapping method (parameters: semantic similarity weight, character matching weight), multiple matching candidates are prioritized, and an optimal matching entry set is obtained; Furthermore, through a normalized tag generation algorithm (parameters: skill library standard coding system, tag mapping table), the original description of the best matching item is transformed into a standardized skill tag, and a normalized skill tag set is output. By combining text regular expression matching and keyword recognition technology with fuzzy alignment processing, the standardized input of professional background in the previous step is transformed into normalized skill labels that can be used for vectorized encoding, thereby achieving accuracy and consistency in skill feature extraction. For example, in a flexible employment recommendation system, when extracting skills from a worker's resume field, the system configures a regular expression matching pattern set containing 50 skill description patterns, such as "Python programming" and "data analysis," with the case-insensitive flag set to true. When using a keyword recognition algorithm, the skill keyword dictionary contains 2000 entries, and the word segmentation granularity is set to fine-grained (accurate to the name of the technical tool or method). During the alignment of candidate skill items with the skill library, the Levenshtein distance threshold is set to 2, and the similarity weighting coefficient is set to 0.7, which corrects the match of "Python programming" to the standard entry "Python programming." In priority ranking, the semantic similarity weight is set to 0.6, and the character matching weight is set to 0.4, ultimately matching "SQL data processing" to the skill library entry "SQL programming" with the highest ranking priority. The normalized label generation algorithm calls the standard coding system of the skill library to convert "Python programming" into label code SK001 and "SQL programming" into label code SK002, and outputs a set of normalized skill labels {SK001, SK002}. This set will be used as the input to the pre-trained occupational semantic model encoder in the subsequent S1.3 step to achieve high-dimensional vectorization processing and ensure that the representation of skill features in the semantic space is stable and consistent. S1.3: Call a pre-trained occupational semantic model (such as JobBERT), input each skill in the normalized skill tag set into the encoder of the model, and perform forward propagation operation to obtain the distributed vector representation of each skill in the high-dimensional semantic space; S1.4: Aggregate the high-dimensional semantic vector representations of each skill, and use a weighted average or attention mechanism to fuse multiple skill vectors of the worker to generate a comprehensive semantic vector that represents the overall skill characteristics of the worker, which serves as its initial representation in the semantic space. S1.5: Normalize the comprehensive semantic vector and scale each dimension of the vector based on the L2 norm to eliminate dimensional differences and improve the stability of subsequent similarity calculation, and output a standardized high-dimensional semantic vector representation of skills that is adapted to downstream graph structure modeling and matching score calculation.

[0011] Step S2: Based on the high-dimensional semantic vector representation of the skills and the industry co-occurrence statistics in the standardized skill base, calculate the semantic similarity between skills and the cross-job co-occurrence frequency, and construct a skill semantic transfer graph, where nodes are standardized skill items and edge weights reflect the potential transfer strength between skills. Specifically, this includes: S2.1: Based on the high-dimensional semantic vector representation of skills generated by the pre-trained occupational semantic model, the semantic similarity score between each skill node is calculated using the cosine similarity algorithm to quantify the closeness of different skills in the semantic space and obtain the skill semantic similarity matrix. Based on the high-dimensional semantic vector representation of workers' skills generated by the pre-trained occupational semantic model, the cosine similarity algorithm (parameters: vector dimension = 768, distance metric = cosine) is used to quantify the closeness of each skill node in the semantic space. Furthermore, through vector dot product and norm normalization, the semantic similarity score between skill nodes is calculated using the following formula:

[0012] in, and These represent the high-dimensional semantic vectors of the two skill nodes, and The L2 norm of the corresponding vector. The output is a semantic similarity score; Furthermore, through batch matrix operations, pairwise similarity calculations are performed on the vector representations of all skill nodes, generating a scale of... × The skill semantic similarity matrix is ​​obtained, and the diagonal elements of the matrix are always equal. To comply with self-similarity logic constraints; Furthermore, the Gaussian smoothing kernel function is used to locally smooth the similarity matrix, and the parameter σ is dynamically adjusted according to the variance of the skill vector distribution to suppress the noise influence of isolated high similarity values ​​and enhance the continuity of the neighboring skill structure. Furthermore, normalization is used to map each element of the smoothed similarity matrix to the [0,1] interval, which facilitates subsequent weighted fusion with cross-job co-occurrence frequency features and achieves dimensional consistency. Through the above cosine similarity calculation and matrix normalization process, the high-dimensional semantic vector of skills in the previous step is transformed into a semantic similarity matrix of skills that can be directly used for graph structure modeling, thereby achieving accurate quantification of potential transfer relationships between skills. For example, in an implementation scenario containing 500 standardized skill items, the input condition is that each skill is encoded as a 768-dimensional semantic vector by the JobBERT model. First, a cosine similarity formula is used to perform pairwise calculations on all skill vectors, generating an initial 500×500 similarity matrix. The diagonal elements of this matrix are fixed at 1, and the off-diagonal elements are distributed between [0.15, 0.92]. Then, a Gaussian smoothing kernel with σ=0.05 is selected to smooth the matrix element-wise, suppressing high similarity biases appearing in isolated skills of a single industry label and reducing the concentration of marginal values. Next, linear normalization is performed on the smoothed matrix, mapping the maximum similarity value to 1 and the minimum value to 0. The resulting skill semantic similarity matrix exhibits a continuous value band distribution in cross-domain node regions, which is beneficial for weighted fusion with cross-job co-occurrence frequency vectors in step S2.3. Test results show that the similarity matrix processed in this way has significantly improved the connectivity effectiveness in cross-domain skill diffusion tasks, enhanced the coherence of migration path identification, and greatly improved the accuracy and stability of subsequent matching recommendations. S2.2: Extract historical recruitment data for multiple positions from the standardized skill base, count the frequency of joint occurrence of paired skills in different industries and job categories, calculate the cross-job co-occurrence frequency score between skills based on the TF-IDF weighted co-occurrence model, and generate a skill co-occurrence intensity feature vector; S2.3: The skill semantic similarity matrix and the skill co-occurrence intensity feature vector are weighted and fused, and a skill transfer intensity score is generated based on a linear combination function. The weight coefficients are determined by regression learning on historical matching success rate data to balance the impact of semantic similarity and practical application synergy on skill transfer. The input conditions are: the skill semantic similarity matrix obtained by S2.1 and the skill co-occurrence intensity feature vector obtained by S2.2, and the node index structure of the two based on the same standardized skill set is consistent; A weighted fusion method is used (parameter: fusion weight coefficient vector). and This allows for the linear combination of skill semantic similarity and skill co-occurrence intensity within the same scoring space. Furthermore, through a linear combination function (input: skill semantic similarity matrix) Skill co-occurrence intensity feature vector Parameter: Weighting coefficient and This will enable the comprehensive skill transfer strength of each pair of skill nodes. Calculate and obtain the comprehensive skill transfer intensity score matrix. The formula is as follows:

[0013] in, Assess the overall transferability strength of skills. This is the semantic similarity weight coefficient. For co-occurrence intensity weighting coefficients, These are the unit values ​​of the skill semantic similarity matrix. This represents the corresponding unit value for the skill co-occurrence intensity characteristic; Furthermore, ordinary least squares regression (OLS) is employed (input: historical matching success rate data Y; features: skill semantic similarity and skill co-occurrence strength) to implement the weight coefficients. and Data-driven estimation is used to generate the optimal fusion ratio, ensuring that the goodness of fit of the fusion result to the matching prediction meets the preset standard.

[0014] Furthermore, the robustness of the weighting coefficients is assessed through cross-validation (parameters: K-fold K=5; evaluation metric: root mean square error RMSE), and the robustness of the weighting coefficients is evaluated. and Fine-tuning was performed to balance the contribution of semantic proximity and practical application synergy to skills transfer scoring; Through the above weighted fusion and weighted regression optimization process, the skill semantic similarity and actual co-occurrence intensity of the previous step are transformed into a comprehensive skill transfer intensity scoring matrix, thereby realizing the quantitative representation of the potential transferability between skills. For example, in a scenario containing 100 standardized skill items, the semantic similarity matrix The element range is 0 to 1, and the co-occurrence intensity eigenvector is... The element range is 0 to 0.5. 500 historical matching success rate samples were selected, and OLS regression estimation was applied to obtain the optimal weight coefficients. =0.65, =0.35. Through the formula:

[0015] Calculate the comprehensive skill transfer intensity score The values ​​ranged from 0.02 to 0.85. After 5-fold cross-validation, the RMSE decreased significantly, validating the rationality of the fusion weight setting. In the subsequent S2.4 graph construction process, only [the following values ​​were retained]. Skill pairs with a value higher than 0.6 are used as connecting edges, which improves the high relevance of the migration graph structure and the availability of cross-domain paths; S2.4: Using standardized skill items as nodes, a skill semantic transfer graph is constructed using skill transfer intensity scores as edge weights. Sparsity processing is used to retain the edges with transfer intensity higher than the dynamic threshold, forming a directional weighted heterogeneous network structure to represent the potential transfer relationship of skills between career paths. S2.5: Perform local connectivity optimization on the constructed skill semantic transfer graph, identify key hub skill nodes based on the PageRank algorithm, and enhance their connection weights with adjacent nodes to improve the logical coherence and recommendation robustness of the graph structure in the cross-domain diffusion process.

[0016] like Figure 2 As shown, step S3 involves inputting the core skill requirements of the target job position into the skill semantic transfer graph, executing a graph diffusion algorithm to identify cross-domain related skill subgraphs connected to the core skills, and generating an expanded matching candidate set containing potential transferable skill paths. Specifically, this includes: S3.1: Based on the predefined standardized skill library and job description specifications, extract the set of core skills required for the target job, and perform terminology normalization on each skill to obtain a unified set of core skill node inputs as the initial activation source for graph diffusion; S3.2: Map the core skill node input set to the corresponding node in the skill semantic transfer graph, and initialize the propagation weight of each adjacent edge based on the semantic similarity between skills and the industry co-occurrence frequency represented by the edge weight in the graph, to form a topological connection relationship with semantic strength identification. The core skill node input set with unified encoding is used as the operation object. The node mapping function (parameters: node unique identifier, skill encoding index) is used to realize the accurate correspondence between the core skill node and the corresponding standardized skill node in the skill semantic transfer graph. Furthermore, by using the adjacency matrix retrieval method (parameters: storage structure of the skill semantic transfer graph, node index range), the direct adjacent skill nodes of the core node can be quickly extracted, and the edge weight data of these adjacent nodes can be obtained. Furthermore, using the edge weight parsing function (parameters: semantic similarity value, industry co-occurrence frequency value), the edge weight data is decomposed into semantic strength components and co-occurrence strength components, and a basic parameter matrix for propagation weight initialization is generated; Furthermore, the propagation weight normalization algorithm (parameters: semantic strength component, co-occurrence strength component, normalization constant) is invoked to calculate the initial propagation weight of each adjacent edge according to the set semantic and co-occurrence weights, as shown in the following formula:

[0017] in, Let i be the initial propagation weight between node i and node j. The semantic similarity value. This represents the industry co-occurrence frequency value. and The weighting coefficient is determined based on historical matching success rates; Furthermore, a topological relationship annotation function (parameters: node mapping table, propagation weight matrix) is used to explicitly annotate the topological connection structure of core skill nodes in the skill semantic transfer graph, and a semantic strength label is attached to each connection edge to form a topological connection relationship with semantic strength identifier; Through the above node mapping, adjacent edge weight extraction, edge weight decomposition, propagation weight normalization and topology annotation processing methods, the core skill node input set is transformed into topological connection data with semantic intensity labels, realizing the initial structural preparation for cross-domain skill diffusion. For example, in a flexible employment scenario, the core skill node input set includes two skills: "Python Development" (SK001) and "Statistical Modeling" (SK015). The node mapping function maps SK001 to migration graph node N045 and SK015 to migration graph node N210 based on unique identifiers. The adjacency matrix retrieval method extracts the direct adjacent nodes N078 and N121 of N045 and the direct adjacent nodes N087 and N156 of N210 from the sparse matrix structure of the migration graph, and reads the corresponding edge weight data. For example, the edge weight between N045 and N078 is 0.82, and the edge weight between N210 and N156 is 0.76. The edge weight parsing function decomposes 0.82 into a semantic strength component of 0.65 and a co-occurrence strength component of 0.17, and decomposes 0.76 into a semantic strength component of 0.54 and a co-occurrence strength component of 0.22. The propagation weight normalization algorithm is set... =0.6、 =0.4, then the initial propagation weights of N045 and N078 are calculated as follows: =0.476, the initial propagation weights of N210 and N156 are calculated as follows: =0.416. The topological relationship labeling function attaches the above propagation weights to the relevant edges of the migration graph, and labels the edges with a value higher than 0.45 as "high semantic strength" and the rest as "medium semantic strength" in the visualization relationship table. Finally, a topological connection relationship matrix with semantic strength labels is formed, which provides an accurate initial weight structure for subsequent cross-domain diffusion calculations; S3.3: Based on the initial propagation weights, execute the multi-hop graph diffusion algorithm to pass the activation signal to the adjacent skill nodes layer by layer, and calculate the cumulative activation value of the nodes in each hop to simulate the dynamic extension process of skill influence in the occupational network. S3.4: Based on the set activation threshold and maximum diffusion depth constraints, truncate the low response region and retain the skill subgraph structure with high activation intensity, and extract the set of non-directly related skill nodes that have indirect but significant semantic paths with the core skill. Based on the cumulative activation value matrix of each skill node output by the multi-hop graph diffusion algorithm, an activation threshold filtering method (parameter: threshold γ, derived from the mean plus standard deviation of historical matching success rates) is used to remove low-response nodes. Furthermore, through the diffusion depth truncation algorithm (parameter: maximum depth) Based on the experience distribution of job skill migration paths, the system can perform structural pruning of node sets that exceed the preset diffusion radius and obtain a preliminary high-activation skill subgraph data structure. Furthermore, by using a connectivity-preserving optimization method (parameter: retain the connected components of core skill nodes and discard isolated nodes), the validity of the high-activation subgraph topology is verified, and a skill subgraph set with optimized topology is generated. Furthermore, a semantic path filtering algorithm (parameter: path length 2 to...) is used. (Edge weight ≥ migration strength threshold) to achieve indirect and significant semantic path identification from core skills to edge skills within the subgraph, and generate a candidate list of indirect related skill nodes; By using a node set extraction processing method, the path filtering results of the previous step are transformed into a set of non-directly related skill nodes with unique node identifiers, activation values, semantic path lengths, and edge weights, thereby achieving the structured extraction effect of cross-domain related skills. For example, in the matching process for the job "User Profile Modeling Engineer" on a flexible employment platform, the core skill set includes Python, statistical modeling, and data cleaning. The activation threshold γ is set to 0.65, which is derived from the average activation value plus standard deviation of successful cross-domain matching cases on the platform over the past year, and the maximum diffusion depth. Set to 3. Among the skill nodes generated by multi-hop diffusion, the activation value of the node "Business Data Analysis" is 0.78, the diffusion path length is 2, and the edge weight migration strength is 0.72; the activation value of the node "Market Research" is 0.68, the path length is 3, and the edge weight migration strength is 0.69; the activation value of the node "Advertising Strategy" is 0.54, which does not meet the threshold and is therefore truncated. Connectivity preservation optimization is used to retain complete subgraphs connected to Python and Statistical Modeling, while discarding node groups isolated only by Data Cleaning. Semantic path filtering locks in two paths: Python → Business Data Analysis and Statistical Modeling → Market Research, both satisfying an edge weight ≥ 0.65. The final set of indirectly related skill nodes includes both Business Data Analysis and Market Research, with records including node ID, activation value (0.78 and 0.68), path length (2 and 3), and edge weight characteristics (0.72 and 0.69). These serve as input for subsequent path backtracking analysis, effectively improving the coverage and accuracy of the cross-domain matching candidate set. S3.5: Perform path backtracking analysis on the high activation intensity skill subgraph, reconstruct the multi-hop migration path sequence from core skills to edge-related skills, and generate an extended matching candidate set containing potential transferable skill paths, as the structured input basis for cross-domain matching evidence derivation; For the node and edge data of the high activation intensity skill subgraph, a path backtracking algorithm based on depth-first search is adopted (parameters: the starting node set is the core skill node, and the maximum backtracking depth is the diffusion depth constraint set in the previous order) to realize the layer-by-layer reverse parsing and structural reconstruction of the skill path; Furthermore, through the path node index mapping table (parameter: bidirectional mapping relationship between node ID and skill tag), the semantic reverse solution of node identifier to standardized skill item is realized during the backtracking process, and path sequence data containing node arrangement order and edge weight value is obtained; Furthermore, a path weight accumulation calculation method is adopted (parameter: edge weights are derived from a migration strength score jointly weighted by semantic similarity and cross-job co-occurrence frequency) to achieve a quantitative assessment of the overall migration capability of each backtracking path and generate a total path migration strength index; the formula is as follows:

[0018] in, The total migration intensity of the path. Let e ​​be the weight of the e-th edge in the path. For edge order counting; Furthermore, a path redundancy filtering algorithm is adopted (parameter: the similar path judgment threshold is based on the intersection ratio of path nodes, set to 0.8) to remove duplicate or highly similar migration paths and retain the path set with high structural diversity, so as to improve the coverage and interpretability of cross-domain matching. Furthermore, through the path feature structured coding method (parameters: node skill label, edge weight migration strength, hop count), the multi-hop migration path after redundancy removal is transformed into an extended matching candidate set structure entry that can be used for subsequent matching evidence derivation, forming a comprehensive record including source skill, target skill, path sequence and total migration strength; By using path backtracking algorithms and structured coding processing, the high-activation skill subgraph from the previous step is transformed into a complete cross-domain migration path data set, enabling the accurate construction and traceability assurance of the expanded matching candidate set. For example, in a financial data analysis job matching scenario, the core skill node set includes "Python data processing" and "statistical modeling," with a maximum backtracking depth of 3. Using a depth-first search backtracking algorithm, the path is traced outward from the "Python data processing" node. The first example path is Python data processing → data visualization → interactive front-end design, with edge weights of 0.92 and 0.85 respectively. The total path migration strength is calculated using the formula: The result was 1.77. Path nodes were parsed into standard skill tags using the index table, and the path sequence was recorded as [Python Data Processing, Data Visualization, Interactive Front-End Design]. During the redundancy removal stage, the intersection ratio of this path with another path, Python Data Processing → Data Visualization → Front-End UI Design, reached 0.66, which is below the 0.8 threshold, so it was retained. Finally, the path was encoded into a structural entry: Source Skill = Python Data Processing, Target Skill = Interactive Front-End Design, Path Hops = 2, Total Transfer Strength = 1.77, and included in the expanded matching candidate set. In system validation, this path was successfully used for cross-industry technical job recommendation, and the output results showed significantly improved cross-domain coverage and coherence of the explanatory content.

[0019] like Figure 3 As shown, step S4 involves comparing the high-dimensional semantic vector representation of the worker's skills with the extended matching candidate set to locate cross-domain skill nodes that the worker possesses but are not directly related to, thereby generating a cross-domain skill matching evidence set. Specifically, this includes: S4.1: Based on the high-dimensional semantic vector representation of skills generated from the worker's historical work experience, project experience and skill tags, the cosine similarity calculation model is used to compare each skill vector with the standardized skill nodes in the cross-domain related skill subgraph contained in the extended matching candidate set one by one, so as to identify skill pairs with potential matching relationships in the semantic space and obtain a preliminary skill matching similarity score. S4.2: Based on the preliminary skill matching similarity score, and combined with the predefined skill category attribution information in the standardized skill library, select skill items that belong to the core skill domain of the target position but have a semantic similarity higher than a set threshold, as a candidate set of non-directly related cross-domain skills possessed by the worker, and exclude technical capabilities that are completely unrelated to or highly deviate from the current job requirements. Based on the preliminary skill matching similarity score and the predefined skill category attribution information in the standardized skill library, a skill category comparison method (parameters: worker skill tag set, job core skill classification coding table) is used to realize the function of skill domain attribution discrimination; Furthermore, by using a semantic similarity threshold filtering algorithm (parameter: set threshold τ, dynamically adjusted according to the matching similarity matrix), the matching results are automatically filtered, and a set of skill candidates that satisfy semantic similarity > τ is obtained; Furthermore, a category exclusion operation method is adopted (parameters: target job core skill domain coding set, worker skill candidate set) to extract skill items outside the core skill domain and generate cross-domain skill intermediate candidate vectors; Furthermore, by applying a technical capability deviation detection algorithm (parameter: Euclidean distance between the skill vector and the target job requirement vector), the algorithm eliminates candidate skills that are completely irrelevant to the job requirements or have a high degree of deviation, and generates a cross-domain skill candidate set with acceptable deviation. By using a candidate set purification process, the screening results from the previous step are transformed into a candidate set of non-directly related cross-domain skills possessed by workers, thereby achieving high accuracy and low misjudgment rate in cross-domain skill positioning. For example, in a flexible employment recommendation system, the set of input worker skill tags is {"Python data analysis", "Java backend development", "UI design"}, and the set of core skills for the target position is {"market research", "user profiling modeling"}. In the standardized skill library, "Python data analysis" belongs to the data science field, "Java backend development" belongs to the software engineering field, and "UI design" belongs to the visual design field. The semantic similarity threshold τ is set to... Calculate the similarity matrix between worker skills and the extended matching candidate skill set, where the similarity between "Python data analysis" and "user profile modeling" is 1. The similarity between "UI design" and "market research" is: The similarity between "Java backend development" and "data pipeline construction" is: Threshold filtering retains samples with similarity higher than [a certain threshold]. Skill pairs were used to obtain an initial screening set {"Python data analysis", "Java backend development"}. In the category elimination stage, tags highly consistent with the data science field and potentially constituting core skills were removed, retaining "Java backend development" as a cross-domain skill candidate. In the deviation detection, the Euclidean distance between the "Java backend development" vector and the job requirement vector was calculated. Below the set deviation limit The system determines that the skill has acceptable transferability potential to the job requirements. The final output is a purified cross-domain skill candidate set {"Java backend development"}, which shows a significantly improved transferability score in subsequent matching algorithms, resulting in a substantial increase in system recommendation accuracy. S4.3: Based on the edge weight parameters already constructed in the skill semantic transfer graph, namely the inter-skill transfer strength jointly weighted by semantic similarity and industry co-occurrence frequency, assign migration path strength values ​​to each cross-domain skill in the candidate set, calculate its support strength for migration from the source job scenario to the target job scenario, and generate a weighted cross-domain skill transfer support vector. S4.4: Align and map the cross-domain skill transfer support vector with the corresponding dimension in the high-dimensional semantic vector of the worker's skills to confirm the factual basis that the worker actually masters the cross-domain skill, and verify the simultaneous validity of the skill mastery status and the strength of the transfer path through logic and operation to form a cross-domain skill matching evidence tuple with dual support conditions. Using cross-domain skill transfer support vectors as the input feature set, a dimension index matching algorithm (parameters: unique encoding table of skill labels, vector dimension mapping table) is adopted to achieve accurate alignment and mapping between the corresponding dimensions of cross-domain skill support vectors and high-dimensional semantic vectors of workers' skills. Furthermore, by using the Boolean state detection method (parameters: skill mastery status identifier bit, threshold for non-zero elements of skill vector), the actual mastery status of workers' skill vectors in each dimension after alignment is verified, and the Boolean matrix data results of skill mastery status are obtained. Furthermore, a dual-condition joint verification algorithm is adopted (parameter: migration path strength threshold). A Boolean matrix of skill mastery status is used to perform logical verification of the simultaneous validity of skill mastery status and migration path strength, and to generate a set of skill indexes that meet the conditions. Furthermore, based on the skill index set, an evidence tuple constructor is applied (parameters: worker ID, original skill tag, target associated skill tag, semantic similarity value). Migration strength score (Based on matching type), it enables the creation of structured cross-domain skill matching evidence tuples and obtains a sequence of evidence tuples; By combining association mapping and conditional verification algorithms, the migration support data from the previous step is transformed into a skill matching evidence tuple supported by both factual knowledge and path strength, thereby achieving high credibility in the construction of cross-domain skill matching evidence. For example, a unique skill tag encoding table is used to index the dimensions of the cross-domain skill transfer support vector, with a total of 512 dimensions. The corresponding high-dimensional semantic vector of worker skills is also 512-dimensional. During the mapping process, consistency of the encoding index is used to complete a one-to-one correspondence between the 512 dimensions. In the fact-checking stage, the skill mastery status threshold is set to consider a vector element value greater than 0.12 as a mastery status, generating a 512-bit Boolean matrix where the proportion of true mastery status is 0.35. In the dual-condition joint verification, the migration path strength threshold is... Setting the threshold to 0.65, a joint judgment is performed on each dimension, retaining only skill indices that simultaneously satisfy the condition of "mastery status is true" and "migration path strength is greater than 0.65," resulting in a total of 58 valid skill indices. Subsequently, the evidence tuple constructor is called, setting the worker ID to 100238, the original skill tag (e.g., "Python data analysis"), the target associated skill tag (e.g., "user profile modeling"), and the semantic similarity value... Take 0.82, migration strength score Using a value of 0.71 and labeling the matching criteria as "cross-domain similarity transfer," 58 structured cross-domain skill matching evidence tuples were generated. In the system evaluation, these evidence tuples all demonstrated significant support for the matching results in the subsequent counterfactual perturbation analysis, thereby improving the interpretability of cross-domain skill matching. S4.5: Summarize all cross-domain skill matching evidence tuples that meet the conditions to construct a structured cross-domain skill matching evidence set. Each record includes worker ID, original skill item, target associated skill item, semantic similarity value, migration strength score and matching basis type, providing a traceable and quantifiable input data foundation for subsequent counterfactual perturbation analysis. Using the high-dimensional semantic vector representation of workers' skills and the cross-domain skill matching evidence tuples with confirmed status in the extended matching candidate set as input, a structured record generation method (parameters: field template definition, data type constraints) is adopted to map the core elements of each evidence tuple to a predefined field structure; Furthermore, by using a field mapping verification algorithm (parameters: worker ID validity rule, skill library code consistency rule), the logical consistency verification of worker ID and skill item code in the input tuple is achieved, and the field validity verification result is obtained. Furthermore, through the numerical calculation module (parameters: semantic similarity calculation formula, migration strength weight formula), the semantic similarity value and migration strength score of each record are normalized and a standardized numerical vector that can be directly compared is generated.

[0020] in, This is the original similarity value. This is the mean offset. and These represent the maximum and minimum limits of the similarity value, respectively. Furthermore, by using a matching basis type labeling algorithm (parameters: evidence source classification rules, migration path existence identifier), the matching source and logical chain type of the evidence tuple are encoded into enumeration labels, and a matching basis type field is generated. By using evidence set aggregation processing (parameters: aggregation strategy = grouped by worker ID, sorting basis = decreasing migration intensity score), the results of the previous step are transformed into a structured cross-domain skill matching evidence set organized by the worker dimension, thus providing a traceable and quantifiable input data foundation for subsequent counterfactual perturbation analysis; For example, in the cross-domain matching module of a flexible employment platform, the input data includes a comprehensive semantic skill vector for worker ID 10025, and three cross-domain skill tuples identified in the extended matching candidate set: {"Python script writing" → "data profiling modeling", similarity value 0.83, migration strength score 0.72, matching basis type "semantic + co-occurrence"}, {"basic statistics" → "user behavior analysis", similarity value 0.79, migration strength score 0.68, matching basis type "semantic"}, {"SQL optimization" → "data modeling", similarity value 0.81, migration strength score 0.74, matching basis type "co-occurrence"}. In the field mapping verification algorithm, all skill codes are true in the consistency check with the standardized skill library entries. After normalization, the similarity value vector is {0.812, 0.772, 0.794}, and the migration strength vector is {0.707, 0.668, 0.727}, where the parameters of the normalization formula are taken as follows: =1.00, =0.60. Based on the matching source classification rules, the system labels the three records as Type A, Type B, and Type C respectively, and sorts them in descending order of migration intensity score, generating a structured cross-domain skill matching evidence set corresponding to worker ID10025. The corresponding fields include worker ID, original skill item, target associated skill item, normalized similarity value, normalized migration intensity score, and matching basis type label. In the verification test, the field completeness and data consistency of this evidence set were normal, meeting the input requirements for subsequent counterfactual perturbation analysis, and significantly improving the accuracy of key skill identification in the analysis phase.

[0021] Step S5: Based on the cross-domain skill matching evidence set, the counterfactual perturbation analysis method is used to perform virtual removal operations on each dimension of the worker's skill vector in sequence, calculate the change in matching score after each perturbation, identify key supporting skills that significantly affect the matching results, and form a causal sensitivity feature sequence. Specifically, this includes: S5.1: Based on the high-dimensional semantic vector representation of workers' skills output by the pre-trained occupational semantic model, obtain the set of indirectly related skill nodes that constitute the evidence set of cross-domain skill matching, and use them as the object of counterfactual analysis to determine their potential contribution in the current employment demand matching process; S5.2: Perform item-by-item virtual removal processing on each dimension of the skill semantic vector corresponding to the non-directly related skill node, that is, perturb a specific dimension in the original vector space by setting it to zero or injecting noise to generate a perturbed skill vector variant to simulate the representation state under the condition of skill absence. S5.3: The perturbed skill vector variant is re-input into the matching scoring function. Based on the correlation strength between the skill semantic transfer graph diffusion path and the core job skills, the matching degree is recalculated to obtain a new matching score under the perturbed condition, which serves as a benchmark for evaluating the importance of skills. S5.4: Calculate the difference between the original matching score and the perturbation matching score, and generate a skill perturbation sensitivity index based on the difference to quantify the impact of each removed skill on the overall matching result and obtain a preliminary skill contribution ranking sequence. Using the original and perturbed output values ​​of the matching scoring function as input conditions, a difference calculation method is employed (parameter: original matching score). Matching score with perturbation This enables a quantitative assessment of the change in matching results before and after perturbation for each skill. Furthermore, the skill perturbation sensitivity index is initially generated through the absolute difference calculation formula (parameter: the sign of the difference is ignored), as follows:

[0022] in, As a sensitivity indicator, The original matching score. The score is the matching score after perturbation; Furthermore, a normalization method (parameters: maximum and minimum sensitivity values) is employed to achieve comparability of different skill sensitivity indices under a unified dimension, resulting in a normalized sensitivity sequence. The normalization formula is as follows:

[0023] in, The normalized sensitivity index value. and These are the maximum and minimum values ​​in the sensitivity index sequence, respectively. Furthermore, a sorting algorithm (parameters: descending order, index corresponding to skill ID) is used to sort the skill contribution priority of the normalized sensitivity sequence and generate a preliminary skill contribution sorting sequence. By combining differential calculation, normalization processing and ranking algorithms, the perturbation matching score result of the previous step is transformed into a quantitative and comparable skill perturbation sensitivity index, so as to achieve the expected technical effect of recognizing the relative importance of the skill in the matching degree contribution. For example, in a flexible employment recommendation instance, the input is the original matching score. Perturbation matching score obtained after removing the skill "Python" Sensitivity index can be obtained by using difference calculation. Among the five cross-domain skills of the same worker, the highest sensitivity value is... The minimum value is The sensitivity index for this skill, obtained after normalization, is:

[0024] The calculation result is It ranked second in descending order. The results show that this skill has a significant impact on changes in matching degree and should be presented as a core supporting skill in subsequent recommendation explanations, as it can significantly improve the credibility of cross-domain matching and the willingness to adopt recommendations. S5.5: Based on the preset impact threshold, key skill nodes with differences exceeding the critical value are selected, marked as causal sensitivity features, and their skill names, migration path sources, and sensitivity scores are integrated to form a structured causal sensitivity feature sequence, which serves as the core evidence set supporting interpretable recommendations. Based on the preliminary skill contribution ranking sequence of the skill disturbance sensitivity index, a threshold filtering algorithm (parameter: influence threshold = system preset value) is used to filter the difference quantity conditionally; Furthermore, by using a logical judgment method (parameter: difference amount > impact threshold), the key skill node set is extracted, and skill node identification data that meets the sensitivity significance condition is obtained; Furthermore, by using field aggregation processing methods (parameters: skill name, migration path source, sensitivity score), the integration of multi-source skill attributes is achieved, and a synthetic record structure containing explicit and implicit skill features is generated. Furthermore, through a data structuring transformation algorithm (parameter: causal sensitivity feature field template), the skill name, migration path source, and sensitivity score are standardized and encoded in a unified mode, generating a causal sensitivity feature sequence that can be used for downstream natural language generation and interpretable path construction. By constructing the aforementioned feature sequences, the sensitivity analysis results from the previous step are transformed into structured, core, and interpretable recommendation evidence, achieving the expected technical effect of transparent explanation of the output in cross-domain skill matching recommendations. For example, in a cross-domain matching scenario involving data analysis and user profiling positions, the system finds that the matching score difference for a worker's skill "Python programming" under perturbation conditions is 0.42, while the system's preset influence threshold is 0.35. A threshold filtering algorithm is used to filter all skill differences, resulting in skill nodes that meet the criteria, including "Python programming" and "data visualization." Logical judgment methods are used to confirm the significance of the sensitivity of these skills, and the skill name and the migration path source in the skill semantic migration graph are recorded, such as "migration from data analysis to user profiling modeling."

[0025] A field aggregation method is used to integrate skill names, migration path sources, and sensitivity scores, such as "Python Programming | Data Analysis → User Profile Modeling | 0.42". After processing with a data structuring transformation algorithm, a causal sensitivity feature sequence is generated. This sequence is then input into the explanation generation module, which can explicitly state in the final recommendation reason that "Python Programming" and "Data Visualization" have a significant supporting role in the matching results in cross-domain migration paths, thereby significantly improving the interpretability and user trust of the matching recommendations.

[0026] Step S6: Input the causal sensitivity feature sequence, skill transfer intensity score, and job requirement keywords into a template-guided natural language generator to generate a structured explanatory element set and output human-readable recommendation reason text. Specifically, this includes: S6.1: Based on the causal sensitivity feature sequence output by the previous steps, extract the key supporting skill identifiers that significantly affect the change in matching score, use them as the core argumentation basis in the interpretable path, and generate a set of key supporting skills for the construction of subject and action subject in subsequent natural language generation. S6.2: For each skill in the set of key support skills, combine its edge weight in the skill semantic transfer graph, i.e. skill transfer strength score, and perform quantitative grading. Use a preset explanation strength mapping function to convert it into descriptive terms (such as 'strong transfer ability' or 'high relevance') to generate a skill transfer strength description label sequence to enhance the professionalism and comprehensibility of the explanation content. S6.3: Extract job requirement keywords from the job requirement information of the target positions, including core responsibility verbs (such as 'modeling' and 'analysis') and technical tool nouns (such as 'Python' and 'SQL'), and perform grammatical role annotation on them based on job semantic coding rules to generate job requirement keyword groups with semantic role tags, which serve as the basic input for the construction of predicates and objects in the natural language generation process; Based on the input of the demand information for the target job positions, the job responsibility parsing module (parameters: job description text, standardized skill library index) is invoked to map the original text to an internal unified coding structure, thereby realizing semantic preprocessing of the demand information. A verb phrase recognition algorithm (parameters: dependency parsing rule base, part-of-speech tagging model) is used to retrieve core responsibility verbs from the requirement information, extract the main work behavior descriptions of the position by matching verb-object structures, and output a set of verbs after word form restoration; Furthermore, by using technical tool identification methods (parameters: technical dictionary, named entity recognition model), the technical tool terms such as programming languages, software tools, and hardware platforms involved in the requirement information are identified, and terminology standardization mapping is completed, unifying the tool terms with different expressions into standard coded identifiers; Furthermore, the job semantic encoding rule engine (parameters: semantic role annotation template set, job semantic classifier) ​​is invoked to perform grammatical role annotation on the verb set and the technical tool set, marking verbs as predicate roles and technical tool nouns as object roles, and generating corresponding semantic modification tags for associated modifiers; By using a structured combination algorithm (parameters: role labeling results, skill transfer background correlation matrix), verbs, technical tools and their modifiers are clustered and sorted according to the role relationship of job requirements, forming job requirement keyword groups with semantic role tags. These keywords serve as the basic input for constructing the predicate and object of the natural language generator, achieving accurate matching between job requirements and skill interpretation. For example, in a target job description text containing "perform user profiling modeling, and use Python and SQL for data processing," the text is first converted into internal encoding by the job responsibility parsing module, identifying the verb "modeling" (coded R001), the tool "Python" (coded T005), and the tool "SQL" (coded T008). The verb phrase recognition algorithm locates "modeling" in the dependency syntax structure and preserves its original form through lexical reconstruction. The technology tool recognition method maps "Python" and "SQL" to standardized technology library entries. The job semantic encoding rule engine labels "modeling" as the predicate, "Python" and "SQL" as objects, and assigns a modification tag C012 to the modifying phrase "user profiling." The structured combination algorithm, based on the relevance of skill transfer background, logically combines "modeling" with "Python" and "SQL" to form the keyword group {predicate: "modeling", object: ["Python", "SQL"], modifier: "user profiling"}. The output of this keyword group provides a clear predicate-object input structure for the natural language generator, enabling the subsequent recommendation reason generation to accurately reflect the core skill requirements of the job. S6.4: Input the set of key supporting skills, the skill transfer intensity description tag sequence, and the job requirement keyword group with semantic role tags into the template-guided natural language generator. Based on the predefined syntax-semantic dual-constraint template library, perform matching and filling operations to generate a set of structured explanatory elements containing subject (worker skills), predicate (transfer behavior), object (target job competency requirements), and modifiers (transfer intensity description), forming candidate explanatory sentences that conform to Chinese expression habits; S6.5: Perform grammatical integrity and semantic consistency checks on the generated candidate explanation sentences, calculate the sentence fluency score using a lightweight language model, and filter out low-confidence outputs; retain the optimal sentence as the final human-readable recommendation reason text, ensuring that the output content accurately reflects the matching logic and has good readability and persuasiveness; Based on the set of candidate explanation sentences output by the template-guided generator, the input conditions include subject skill identifiers, predicate transfer behavior, and object job requirement keywords contained in the set of structured explanation elements; A syntactic integrity detection method (parameter: tree structure comparison rule set based on dependency parsing) is adopted to determine the existence of subject-verb-object structure and the rationality of the position of modifiers for each candidate sentence, and outputs a syntactic integrity label matrix. Furthermore, through a semantic consistency verification method (parameters: based on word vector cosine similarity and terminology normalization dictionary), the domain relevance matching between skill tags and job requirement keywords in candidate sentences is achieved, and a semantic consistency score vector is obtained; Furthermore, a lightweight language model inference method (parameters: model size ≤ 50M parameters, BeamSize=5) is used to perform probabilistic evaluation of the fluency of candidate sentence patterns and generate a set of language fluency scores for each sentence pattern. Furthermore, a confidence filtering algorithm (parameters: syntactic integrity threshold = 0.85, semantic consistency threshold = 0.9, language fluency threshold = 0.8) is used to eliminate candidate sentence patterns below any of the thresholds and generate a set of high-confidence sentence patterns. By using the optimal selection function (parameters: comprehensive score weighting coefficients α=0.4, β=0.3, γ=0.3), the set of high-confidence sentences from the previous step is taken as input, the comprehensive quality score of each sentence is calculated, and the sentence with the highest score is selected as the final human-readable recommendation reason text, so as to achieve the content accurately reflecting the matching logic while maintaining readability and persuasiveness. For example, in a cross-domain skill matching and recommendation scenario, the input structured explanation element set includes the subject skill "data analysis ability," the transfer behavior "transferable to," the object job requirement "user profile modeling job," and the transfer strength description "highly relevant." The dependency parsing rule set detects that the candidate sentence structure has a complete subject, predicate, object, and modifier structure, with a syntactic integrity label value of 0.96. The word vector model calculates the semantic similarity between the skill label and the job requirement keywords to be 0.94, meeting the set semantic consistency threshold. The lightweight language model (BeamSize=5) scores the fluency of this sentence structure at 0.91, meeting the fluency threshold. The confidence filtering algorithm eliminates another sentence structure (fluency 0.78), retaining the current sentence structure for the scoring stage. The comprehensive scoring function is as follows:

[0027] The calculated overall score was 0.939, ranking first in the candidate set. The final recommendation text was "Your data analysis skills are transferable to user profiling modeling positions, as both are highly correlated in handling Python and statistical modeling tasks," achieving a technical effect that accurately reflects the logic of skill transfer while maintaining fluent and professional language.

[0028] Step S7: Bind the recommendation reason text to the corresponding job matching result to form a recommendation item with a dynamic and interpretable path, and provide an interactive expansion mechanism in the user interface to display the matching logic chain. Specifically, this includes: S7.1: The recommendation reason text and the job matching result are structured and encapsulated to generate a composite recommendation data unit containing matching score, job information, worker skill evidence and explanatory text, which serves as the input basis for subsequent binding operations; S7.2: Based on the composite recommendation data unit, perform the recommendation item binding operation, establish a bidirectional association pointer between the recommendation reason text and the corresponding job matching result in the data logic layer, generate recommendation items with dynamic interpretable paths, and ensure that the explanation content can be uniquely traced back to the original matching decision instance; For composite recommendation data units, a logical layer association and binding operation is performed. A two-way pointer establishment method is used (parameters: source data unit identifier, target job matching result identifier, recommendation reason text identifier) ​​to realize the two-way reference relationship between the recommendation reason text and the corresponding job matching result in the data logic layer. Furthermore, a unique identifier mapping algorithm (parameters: worker ID, job ID, recommendation reason ID) is used to achieve uniqueness constraints on recommended items and obtain a traceable matching instance index table. Furthermore, an association consistency verification method (parameters: index table, skill matching evidence set reference path, natural language interpretation binding path) is adopted to realize the consistency verification of the logical layer bidirectional pointers and generate a binding confirmation record that passes the verification. Furthermore, a persistent bound data structure is achieved through a relational data structure construction method (parameters: primary key of recommended items, foreign key of explanatory text, and foreign key of job results), and a bound table index that meets the query optimization conditions is generated; By using bidirectional pointer binding and index generation, the composite recommendation data unit results from the previous step are transformed into uniquely traceable, dynamically explainable recommendation items, achieving a highly reliable association between the recommendation reason text and the matching results at the logical layer. For example, in a flexible employment platform recommendation system, the input composite recommendation data unit includes a matching score of 0.82, job ID P015, worker ID W102, skill matching evidence set path "SK_PATH_78", and recommendation reason text ID T209. When the system performs bidirectional pointer establishment, it forms a bidirectional reference between job ID P015 and recommendation reason text ID T209 at the data logic layer, and uses a unique identifier mapping algorithm to generate a composite primary key.<JPK_W102_P015_T209> The primary key record is then registered in the index table. During the consistency verification phase, the consistency of the skill matching evidence set path "SK_PATH_78" in the job data table and the recommendation reason text table is checked. The verification result is passed, and a binding confirmation record BC_015_T209 is generated. Subsequently, a binding table "RecBindTable" is created in the relational database, where the primary key field is a composite primary key.<JPK_W102_P015_T209> The foreign key fields include the job result foreign key P015 and the explanatory text foreign key T209. This bound table optimizes retrieval performance through a B+ tree index, enabling efficient tracing of any job matching result and its corresponding recommendation reason text. In the test scenario, retrieving the recommendation entry JPK_W102_P015_T209 directly retrieves the original matching decision instance and all relevant explanatory evidence. Verification results show that the bound structure maintains low query latency under high concurrency access, significantly improving the tracing efficiency and stability of the matching logic chain. S7.3: Utilize the front-end rendering engine to perform visual transformation processing on recommended items with dynamic interpretable paths, generating UI components with collapsible / expandable state control functions. By default, the summary information of the matching results is displayed, while the detailed explanation path is hidden, forming the initial interactive view. S7.4: Based on the click event trigger signal on the user interface, execute the explanation path expansion instruction parsing operation, call the DOM tree update mechanism to dynamically load and render the hidden recommendation reason text content, and present the natural language explanation generated by the fusion of causal sensitivity feature sequence, skill transfer intensity score and job requirement keywords as a readable paragraph; S7.5: Capture and label the user's behavioral trajectory during the interactive expansion process to generate operation log data including dwell time, expansion depth, and feedback actions, which will serve as the input for updating edge weight parameters and template weights in the subsequent online model optimization module.

[0029] Step S8: Monitor user feedback on their adoption of recommended items with dynamic interpretable paths, and update the edge weight parameters in the skill semantic transfer graph and the template weights of the natural language generator based on the feedback results, thereby achieving online optimization of the interpretable recommendation model. Specifically, this includes: S8.1: Obtain user interaction behavior log data for recommended items with dynamic interpretable paths, extract user adoption behavior tags based on the log parsing module, the adoption behavior tags include operation sequences such as clicking, viewing details, accepting recommended positions, rejecting recommendations, and actively closing the explanation panel, and generate a structured user feedback behavior sequence to quantify the actual response degree of users to the recommendation results and their explanation content; S8.2: Based on the structured user feedback behavior sequence, calculate the time-weighted statistical index of positive adoption rate and negative ignore rate, use the Bayesian update model to evaluate the explanatory validity score corresponding to each recommendation reason text, and use the score as a backpropagation signal to input into the interpretability quality assessment function to generate skill-explanation coupling strength residuals to identify key explanatory defect patterns that lead to user distrust. S8.3: Based on the skill-explanation coupling strength residual, the gradient descent algorithm is used to fine-tune the edge weight parameters between corresponding skill nodes in the skill semantic transfer graph. The edge weight update direction is determined according to the residual sign, and the update magnitude is jointly adjusted by the learning rate factor and the user behavior confidence level to generate an optimized skill semantic transfer graph, so as to enhance the ability to represent skill transfer relationships on high-frequency adoption paths. S8.4: Map the explanation effectiveness score to the template pool of the natural language generator, and update the selection priority weight of each explanation template based on the REINFORCE algorithm in the reinforcement learning framework. The template with a high score receives a positive reward, and the template with a low score is penalized. This generates a dynamically adjusted template weight distribution to improve the language expression adaptability and persuasiveness of future recommendation reasons. S8.5: Integrate the optimized skill semantic transfer graph with the updated template weight distribution, perform model version consistency verification and trigger the online deployment pipeline, release the new version of the explainable recommendation model to the production environment, and retain historical versions for A / B testing comparison, so as to realize the continuous iteration and closed-loop optimization of the explainable recommendation system.

[0030] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and rules of the present invention should be included within the scope of protection of the present invention.

Claims

1. A flexible two-way matching method for employment based on multi-dimensional profiling and real-time needs, characterized in that: Includes the following steps: S1: Obtain the structured and unstructured resume data of workers, extract skill fragments, perform fuzzy alignment to generate a normalized skill tag set, and vectorize the normalized skill tag set based on a pre-trained occupational semantic model to generate a high-dimensional semantic vector representation of skills. S2: Based on the high-dimensional semantic vector representation of the skills and the industry co-occurrence statistics in the standardized skill library, calculate the semantic similarity between skills and the cross-job co-occurrence frequency, and construct a skill semantic transfer graph; S3: Input the core skill requirements of the target job position into the skill semantic transfer graph, identify cross-domain related skill subgraphs connected to the core skills, and generate an expanded matching candidate set, specifically including: Based on a predefined standardized skill library and job description specifications, the set of core skills required for the target job is extracted, and the terminology of each skill is normalized to obtain the set of core skill node inputs. The core skill node input set is mapped to the corresponding node in the skill semantic transfer graph, and the propagation weight of each adjacent edge is initialized based on the semantic similarity between skills and the industry co-occurrence frequency represented by the edge weight in the skill semantic transfer graph. Based on the propagation weights, a multi-hop graph diffusion algorithm is executed to pass activation signals to adjacent skill nodes layer by layer, and the cumulative activation value of nodes in each hop is calculated. Based on the set activation threshold and maximum diffusion depth constraints, the low response region is truncated while the high activation intensity skill subgraph is retained, and a set of non-directly related skill nodes that have indirect but significant semantic paths with the core skill is extracted. Path backtracking analysis is performed on the high activation intensity skill subgraph to reconstruct the multi-hop migration path sequence from core skills to edge-related skills and generate an expanded matching candidate set; S4: Compare the high-dimensional semantic vector representation of the skill with the extended matching candidate set to locate cross-domain skill nodes and generate a cross-domain skill matching evidence set; S5: Based on the cross-domain skill matching evidence set, perform virtual removal operations on each dimension of the high-dimensional semantic vector representation of the skill in sequence, calculate the change in the matching score after each perturbation, identify key supporting skills that significantly affect the matching results, and form a causal sensitivity feature sequence, specifically including: Based on the high-dimensional semantic vector representation of skills generated in step S1, a set of non-directly related skill nodes that constitute the cross-domain skill matching evidence set is obtained. Perform item-by-item virtual removal processing on each dimension of the skill semantic vector corresponding to the non-directly related skill nodes to generate perturbed skill vector variants; The perturbed skill vector variant is re-input into the matching scoring function. Based on the correlation strength between the skill semantic transfer graph diffusion path and the core skills of the job, the matching degree is recalculated to obtain the perturbed matching score under the perturbed condition. Calculate the difference between the original matching score and the perturbation matching score, generate a skill perturbation sensitivity index based on the difference, normalize the skill perturbation sensitivity index and sort it in descending order to obtain a skill contribution ranking sequence. Based on a preset impact threshold, key skill nodes whose difference exceeds a critical value are selected, marked as causal sensitivity features, and their skill names, migration path sources, and sensitivity scores are integrated to form a structured causal sensitivity feature sequence. S6: Input the causal sensitivity feature sequence, skill transfer intensity score and job requirement keywords into the template-guided natural language generator, and output the recommendation reason text; S7: Bind the recommendation reason text with the corresponding job matching result to form a recommendation item.

2. The flexible employment two-way matching method based on multi-dimensional profiling and real-time needs as described in claim 1, characterized in that, Following step S7, the following is also included: S8: Monitor user feedback on the adoption behavior of the recommended items, and update the edge weight parameters and template weights of the natural language generator in the skill semantic transfer graph based on the feedback results.

3. The flexible employment two-way matching method based on multi-dimensional profiling and real-time needs as described in claim 1, characterized in that, The structured and unstructured resume data specifically includes: structured resume data with explicitly labeled skill tags, and unstructured resume data including historical work experience text and project descriptions.

4. The flexible employment two-way matching method based on multi-dimensional profiling and real-time needs as described in claim 1, characterized in that, The nodes in the skill semantic transfer graph are standardized skill items, and the edge weights are skill transfer intensity scores.

5. The flexible two-way matching method for employment based on multi-dimensional profiling and real-time needs as described in claim 1, characterized in that, Step S4 specifically includes: Based on the high-dimensional semantic vector representation of skills generated in step S1, the cosine similarity calculation model is used to compare each skill vector with the standardized skill nodes in the cross-domain related skill subgraph contained in the extended matching candidate set, identify skill pairs with potential matching relationships in the semantic space, and obtain a preliminary skill matching similarity score. Based on the preliminary skill matching similarity score, and combined with the predefined skill category attribution information in the standardized skill library, skill items that belong to the core skill domain of the target position but have a semantic similarity higher than a set threshold are selected as a candidate set of non-directly related cross-domain skills. Based on the edge weight parameters already constructed in the skill semantic transfer graph, the migration path strength of each cross-domain skill in the non-directly related cross-domain skill candidate set is assigned, the support strength of its migration from the source job scenario to the target job scenario is calculated, and a cross-domain skill migration support vector is generated. Align and map the cross-domain skill transfer support vector with the corresponding dimension in the high-dimensional semantic vector representation of the skill to confirm the factual basis that the worker actually masters the cross-domain skill, and verify the simultaneous validity of skill mastery status and transfer path strength through logic and operation to form a cross-domain skill matching evidence tuple. All cross-domain skill matching evidence tuples that meet the conditions are aggregated to construct a structured cross-domain skill matching evidence set.

6. The flexible employment two-way matching method based on multi-dimensional profiling and real-time needs as described in claim 5, characterized in that, In the structured cross-domain skill matching evidence set, each record includes worker ID, original skill item, target associated skill item, semantic similarity value, migration strength score, and matching basis type.

7. The flexible employment two-way matching method based on multi-dimensional profiling and real-time needs as described in claim 1, characterized in that, The item-by-item virtual removal process specifically involves perturbing a specific dimension in the original vector space by setting it to zero or injecting noise, thereby generating a perturbed skill vector variant.

8. The flexible employment two-way matching method based on multi-dimensional profiling and real-time needs as described in claim 1, characterized in that, The job requirement keywords are extracted from the job requirement information of the target positions, including core responsibility verbs and technical tool nouns.

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