Career development recommendation method based on alumni relationship chain and dynamic employment data

Abstract

The application proposes a career development recommendation method based on alumni relationship chain and dynamic employment data, which comprises the following steps: integrating the academic data of the on-campus student database and the dynamic employment data of the external recruitment platform, realizing the reliable storage and update of the alumni career track through the blockchain technology, analyzing the unstructured text and extracting the standardized field by using the natural language processing technology; constructing a dynamic time window model, classifying and weighting the employment information according to the data timeliness, analyzing the career transition track of alumni of the same major by using the path clustering algorithm, and generating three typical development mode libraries of technology deep cultivation type, management transformation type and cross-industry transition type; constructing an alumni relationship graph from the hierarchical, industry and geographical dimensions, analyzing the position promotion rate and regional aggregation characteristics, identifying the key internal promotion nodes and labeling the associated enterprise resources through the improved PageRank algorithm; and constructing a four-dimensional target model based on the post matching degree, alumni correlation strength, development potential and salary growth space.

Description

Technical Field

[0001] This invention belongs to the field of career development recommendation, and specifically relates to a career development recommendation method based on alumni relationship chains and dynamic employment data. Background Technology

[0002] Currently, career development recommendation methods based on alumni networks and dynamic employment data are an important research direction in the fields of career planning and talent services. Their core objective is to provide job seekers with precise and personalized career development advice by deeply integrating the correlation between educational backgrounds and real-time employment market information. Existing technologies largely rely on static resume matching, single-dimensional skill tag filtering, or collaborative filtering algorithms based on historical data. These methods have gradually revealed significant shortcomings in practical applications.

[0003] First, traditional recommendation models are not good at sensing changes in the dynamic job market. They rely solely on historical recruitment data to build recommendation systems, making it difficult to capture real-time changes such as fluctuations in industry demand and the emergence of new jobs. For example, the exponential growth in talent demand in emerging fields such as artificial intelligence and carbon neutrality often takes 6-12 months to be reflected in the recommendation system, causing job seekers to miss the best career opportunities.

[0004] Secondly, existing technologies for utilizing social networks remain at the level of superficial association analysis, failing to delve into the hidden career development path value within alumni networks. Deeper information such as industry distribution characteristics, job promotion patterns, and complementary skills among alumni has not been effectively extracted. Studies show that professionals with strong alumni connections are 3.2 times more likely to receive referrals than ordinary job seekers, but existing systems utilize less than 15% of such resources.

[0005] Furthermore, there are bottlenecks in multi-source heterogeneous data fusion technology. Educational background data, corporate recruitment demand data, and personal career trajectory data are scattered across different systems with significantly different update frequencies, making it difficult to guarantee the timeliness and completeness of the data in the recommendation model. A survey of a leading recruitment platform showed that its job information update delay averaged 23 days, resulting in 30% of the recommendation results being out of touch with real-time market demand.

[0006] Furthermore, the accuracy of personalized recommendations is limited by the one-sidedness of feature engineering. Traditional methods overemphasize explicit skill matching (such as programming language proficiency) while neglecting implicit career development factors (such as industry network density and regional industrial policy guidance). This results in a low degree of alignment between recommended positions and job seekers' long-term career plans. User surveys by career consulting agencies show that only 38% of respondents are satisfied with the medium- and long-term planning functions of existing recommendation systems. More seriously, inadequate data privacy protection mechanisms lead to legal risks in the development and utilization of social relationship data. Existing systems often employ coarse-grained permission management and lack dynamic authorization mechanisms when collecting alumni relationship data. EU GDPR compliance audits have found that approximately 67% of career recommendation platforms excessively collect data related to educational backgrounds.

[0007] Finally, the phenomenon of cross-platform data silos restricts the overall optimization capabilities of recommendation systems. Alumni growth data from educational institutions, human resource data from enterprises, and job postings from third-party recruitment platforms have failed to form an effective data circulation ecosystem. Research shows that breaking down data barriers can improve career recommendation accuracy by 42%, but the current technical architecture struggles to achieve secure and reliable collaborative data computation. These shortcomings collectively lead to fundamental problems in existing career recommendation systems, such as recommendation lag, insufficient relevance, and weak planning, urgently requiring breakthroughs through innovative alumni relationship chain mining and dynamic employment data fusion technologies. Summary of the Invention

[0008] This invention proposes a career development recommendation method based on alumni relationship chains and dynamic employment data. This method solves the problems of poor real-time performance, data silos, low recommendation accuracy, high risk of privacy leakage, and insufficient user participation in existing career recommendation systems. Through the collaborative innovation of dynamic data fusion, three-dimensional relationship chain mining, privacy computing, and blockchain incentive technology, it realizes intelligent, personalized, and sustainable recommendations for career development paths.

[0009] The technical solution of this invention is implemented as follows: a career development recommendation method based on alumni relationship chains and dynamic employment data, the method comprising the following steps:

[0010] It integrates academic data from the school's student database with dynamic employment data from external recruitment platforms, uses blockchain technology to achieve trusted storage and updating of alumni career trajectories, and utilizes natural language processing technology to parse unstructured text and extract standardized fields.

[0011] A dynamic time window model is constructed, and employment information is processed by weighting according to the timeliness of data. The path clustering algorithm is used to analyze the career leap trajectory of alumni in the same major, and a library of three typical development models is generated: technical in-depth type, management transformation type, and cross-industry leap type.

[0012] We construct alumni relationship graphs based on hierarchical, industry, and geographical dimensions, analyze career advancement rates and regional clustering characteristics, and identify key referral nodes and label related corporate resources using an improved PageRank algorithm.

[0013] A four-dimensional target model is constructed based on job matching degree, alumni association strength, development potential and salary growth space. A reinforcement learning mechanism is deployed to dynamically adjust the recommendation weight and match lower-grade students with alumni who have been promoted quickly as career mentors.

[0014] A federated learning framework is used to achieve cross-domain data collaborative modeling. Noise is added to the relationship chain analysis results to protect privacy. Digital twin professional bodies are generated to simulate different development paths and are displayed through interactive devices. The contribution point algorithm designed in the modeling process is used to evaluate the quality and update frequency of matching data. Based on smart contracts, contribution points can be exchanged for referral opportunities and consulting services. The entire process of point transfer is recorded and an immutable certificate is generated.

[0015] Real-time monitoring of fluctuations in related industries and job competition indices, and setting time periods to trigger alternative path suggestions and update prediction model parameters, forming a closed-loop optimization mechanism.

[0016] Traditional job recommendation systems mainly rely on static resume matching and single-dimensional collaborative filtering algorithms, which have the following core defects: poor data timeliness: existing systems are based on historical recruitment data and cannot capture sudden changes in industry demand, such as the explosive growth of positions in emerging fields such as AI and carbon neutrality, and the dynamic adjustment of corporate hiring standards, resulting in recommendation results lagging behind actual market demand.

[0017] Low utilization of alumni resources: Current technologies for mining alumni networks remain superficial, failing to delve into the patterns of alumni career advancement, such as promotion speed, cross-industry job-hopping paths, regional clustering effects, and complementary skills, resulting in a low success rate for internal referral opportunities. Difficulty in integrating multi-source data: Educational data, corporate data, and personal data are scattered across heterogeneous platforms, with significant differences in data formats, mismatched update frequencies, and conflicting privacy protection requirements, making it impossible for traditional ETL tools to achieve efficient and secure data collaboration.

[0018] The current system suffers from several challenges: **Simplified Recommendation Strategies:** It overemphasizes explicit skill matching, neglecting the long-term impact of career development factors, resulting in low alignment between recommended positions and users' career plans. **Privacy vs. Utility Conflict:** Traditional methods face a dilemma when sharing data: centralized data processing risks leaking sensitive information, while fully localized processing leads to decreased model accuracy. Existing differential privacy schemes can introduce excessive noise, distorting recommendation results. **Lack of User Engagement:** The absence of effective incentive mechanisms results in low alumni data update rates and significant gaps in key career trajectory information, hindering the system's iterative optimization capabilities.

[0019] The technical solutions adopted in this application include: dynamic alignment of multi-source heterogeneous data: addressing the semantic mapping problem between education and employment data, and designing a cross-platform data update synchronization mechanism; deep modeling of complex relationship chains: establishing a hierarchical, industry, and geographical three-dimensional association model to quantify the influence of alumni nodes; privacy-utility balance: achieving data usability without visibility under the federated learning framework, ensuring that the recommendation accuracy loss after adding noise is less than 5%; sustainable ecosystem construction: designing a token incentive mechanism to create a positive cycle between alumni data contribution and resource acquisition, increasing the data update rate to over 75%; and real-time dynamic response: constructing an industry fluctuation monitoring model to trigger strategy adjustments when the job competition index changes abruptly.

[0020] Furthermore, when integrating academic data from the on-campus student database with dynamic employment data from external recruitment platforms, the system connects to the on-campus student database to obtain student status information, course grades, research projects, and competition award data, while simultaneously accessing the external recruitment platform API to collect alumni job changes and skills certification updates in real time. A blockchain-based evidence storage network is constructed to store alumni career trajectory data in a timestamp chain, ensuring that the data is tamper-proof and supports historical version tracing. A dynamic data update mechanism is designed to periodically trigger alumni career information supplementation through an intelligent questionnaire system, and to use natural language processing technology to parse unstructured text descriptions and extract standardized fields.

[0021] Furthermore, the dynamic time window model processes employment data by weighting it according to its timeliness: the weight of data from the most recent month is set to 0.9, and the weight decreases by 0.1 for each additional month. The weight of historical data exceeding 12 months is reset to zero. The model uses a path clustering algorithm to analyze the career advancement trajectories of alumni in the same major, extracts typical development patterns, and constructs a predictive model library to predict future skills demand gaps. It also generates early warning signals by combining alumni skills update data. The model automatically adjusts the built-in parameters of the predictive model library for different career paths based on industry fluctuations.

[0022] Furthermore, when constructing the alumni relationship graph, the promotion rate of alumni positions is analyzed in the hierarchical dimension to identify benchmark nodes for rapid promotion; industry penetration heatmaps are drawn in the industry dimension to detect abrupt changes in penetration rate in emerging fields; regional alumni concentration is marked in the geographical dimension to identify alumni resource-rich areas within the region; the influence of alumni nodes is quantified through the PageRank algorithm to screen key alumni and mark their associated corporate resources.

[0023] Furthermore, after constructing the four-dimensional target model, a reinforcement learning mechanism is deployed to automatically reduce the weight of similar recommendations when users mark "not interested" and to reinforce the associated features of successful job placement cases. A cold start solution mechanism is designed to match lower-grade students with alumni from the same department as career mentors.

[0024] Furthermore, the federated learning framework constructs an encrypted feature alignment matrix to map the academic features of the school database and the career features of the recruitment platform to the same latent space. In the latent space, a differential privacy protection strategy is used to add Laplace noise to the association strength data of alumni relationship chains to ensure that individual career trajectories are irreversible.

[0025] Furthermore, when creating a digital twin professional entity, a benefit table is established by quantifying the time opportunity cost and the difference in expected salary. Based on the professional data of alumni in the industry and related fields, the probability of job promotion, the space for skill enhancement, and the industry risk coefficient are predicted to form analytical data. The benefit table and analytical data are then imported into an interactive device for visualization.

[0026] The beneficial effects of this invention after adopting the above technical solutions are as follows: This method significantly improves the overall efficiency of career recommendation services through technological innovation and systematic design; the dynamic time window model enables real-time capture of changes in industry demand, ensuring that recommendation results closely align with the latest market trends and effectively identify job opportunities in emerging fields; the three-dimensional relationship chain deep mining technology enhances the utilization rate of alumni resources, accurately connects internal referral opportunities, and improves job matching and career development fit; the federated learning framework and differential privacy technology work synergistically to strictly protect sensitive individual information during data collaborative computation, meeting international privacy compliance requirements; the blockchain points incentive mechanism promotes alumni community activity, drives high-quality data updates and a virtuous cycle of resources, and builds a sustainable career service ecosystem; the digital twin simulation system provides visualization. Career path simulation quantifies the differences in short- and long-term benefits among different choices, assisting users in formulating rational career plans; a closed-loop optimization mechanism enables automatic updates of model parameters and dynamic adjustments to strategies, continuously improving the recommendation system's ability to adapt to complex market environments; user experience upgrades: interactive AR display technology lowers the barrier to information understanding, intuitively presenting alumni relationship networks and the distribution of key career resources, improving decision-making efficiency; based on alumni career trajectories, it feeds back into the university's talent cultivation system, promoting deep alignment between professional settings and industry needs, and driving the integration and innovation of the education chain and the industry chain; precise recommendations shorten the job matching cycle, reduce corporate recruitment costs, and simultaneously improve talent retention and job suitability; social value creation: it builds a win-win career development ecosystem, promotes the optimal allocation of talent resources, and contributes to regional economic development and industrial upgrading. This solution breaks through the limitations of traditional recommendation systems through technological innovation, achieving a leapfrog upgrade in career services from static matching to dynamic deduction, and from one-way information transmission to ecological collaborative empowerment. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Example:

[0031] like Figure 1 As shown, the career development recommendation method based on alumni relationship chains and dynamic employment data, in the context of career planning guidance in universities, is implemented as a data-driven, privacy-protected, and dynamically optimized intelligent recommendation system.

[0032] Taking a career guidance system for the computer science major at a comprehensive university as an example: During the system initialization phase, the system first connects to the university's academic affairs database (including student GPA, project experience, and skill certificates) and real-time job data (salary, job requirements, and promotion paths) from external platforms such as LinkedIn and BOSS Zhipin via an API gateway. A distributed data storage network is built using a consortium blockchain. Each participating node (the university, recruitment platforms, and alumni companies) agrees on data update rules through smart contracts. For example, each time an alumnus is promoted, they need to submit an employment certificate signed by the company's HR on the blockchain. The system uses the BERT model to perform entity recognition on the unstructured career descriptions filled in by alumni (such as "leading the design of distributed system architecture"), extracts standardized fields (technology stack: distributed systems; job level: architect), and maps them to the Ontology library.

[0033] In the data preprocessing stage, the dynamic time window model classifies employment information according to timeliness: newly added positions in the past 3 months are assigned a weight of 0.6, data within 1 year is assigned 0.3, and historical data is assigned 0.1. For the career paths of computer science alumni, the path clustering algorithm identifies three typical patterns: technical in-depth development (60% of alumni linearly develop from development engineers to chief technology officers), management transformation (25% switch to product management positions in 5-8 years), and cross-industry leapfrogging (15% transition to fintech or AI healthcare). Each pattern library contains 100+ trajectory nodes with timestamps (e.g., "promoted to senior engineer in 2020 → appointed as AI team leader in 2023").

[0034] During the alumni relationship graph construction phase, the system imported 100,000 alumni records from a CSV file and established a three-layer relationship network based on the Neo4j graph database: hierarchical dimension connecting alumni at different levels within the same company (e.g., Zhang San from the class of 2015 is the department head of Li Si from the class of 2020); industry dimension labeling the alumni's specific fields (cloud computing, blockchain, etc.); and geographical dimension clustering regional clusters such as North China, Southwest China, and the Yangtze River Delta. The improved PageRank algorithm introduced industry popularity factors (cloud computing weight +30%) and promotion acceleration (those who are promoted two levels within 2 years have doubled influence). Wang, the chief researcher of Microsoft Research Asia, was identified as a key node (directly associated with the recruitment channels of 8 AI unicorn companies).

[0035] When the recommendation engine was running, for Li, a third-year student (GPA 3.8, proficient in machine learning), the four-dimensional model calculated his matching degree with alumni: job matching degree 85%, due to his mastery of TensorFlow and PyTorch, alumni association strength 70%, three citations of papers with Zhang, a senior engineer from Baidu who graduated from the same lab, development potential calculated based on Zhang's team's average annual promotion rate of 65%, salary growth space predicted to increase by 200% based on the 5-year salary curve of similar positions, and reinforcement learning agents dynamically adjusted weights based on historical success cases (employment quality of previous students after accepting recommendations), and finally recommended Zhang as the first mentor with cross-industry alternative (Chen, head of financial AI at Ant Group).

[0036] In terms of privacy protection, the federated learning framework distributes data processing to on-campus servers and edge nodes of recruitment platforms. When aggregating the strength of alumni relationships, it adds Laplace noise ε=0.1. The generated digital twin professional body simulates two paths: Path A: becoming an algorithm engineer in 3 years → becoming an AI product manager in 5 years, and Path B: continuing to work in the CV field and becoming a chief scientist. Both paths are shown through VR devices to demonstrate the differences in work scenarios and skill requirements five years later.

[0037] The contribution points system awards points to active alumni based on the data quality assessment module (which detects the frequency of alumni information updates and the completeness of fields). For example, Liu, a cloud architect at Company A, can earn 50 points per month for updating technical stack information. When he accumulates 2,000 points, he can redeem the qualification to refer a senior engineer at Company B. All point transfers are recorded through Hyperledger Fabric and generated with timestamp hash evidence.

[0038] The dynamic optimization module monitors related indicators in real time: when it detects a sudden 40% increase in the job competition index in the autonomous driving industry (due to the opening of large-scale recruitment), the system immediately generates alternative suggestions for students who choose path A (turning to the field of robot perception algorithms) and starts model retraining—using incremental learning to update the LSTM parameters of the job demand prediction module. At the end of each quarter, the improvement of the model's AUC is evaluated through cross-validation, forming a complete closed loop from data collection, intelligent recommendation to feedback optimization.

[0039] After the implementation of the program, the matching rate between the first job and the recommended path for the computer science graduates of the university reached 78%, the average job search cycle was shortened to 2.3 weeks, the success rate of alumni referrals increased to 2.5 times that of traditional channels, and no sensitive personal information was leaked throughout the process. The program met GDPR compliance requirements after being audited by a third party. The contribution points system has increased the alumni information update rate from 12% to 67%, significantly enhancing the dynamic adaptability of the system.

[0040] When integrating academic data from the on-campus student database with dynamic employment data from external recruitment platforms, the system connects to the on-campus student database to obtain student status information, course grades, research projects, and competition awards data. Simultaneously, it accesses the API of external recruitment platforms to collect alumni job changes and skills certification updates in real time. A blockchain-based evidence storage network is constructed to store alumni career trajectory data in a timestamp chain, ensuring that the data is tamper-proof and supports historical version tracing. A dynamic data update mechanism is designed to periodically trigger alumni career information supplementation through an intelligent questionnaire system, and to use natural language processing technology to parse unstructured text descriptions and extract standardized fields.

[0041] When deploying the data integration module in the university's career guidance center, the system connects to the university's Oracle database via the OAuth2.0 protocol, synchronizing student status information, course grades, research projects, and competition awards data every morning. Simultaneously, it calls the RESTful APIs of BOSS Zhipin and Liepin platforms to obtain alumni job change information and skills certification updates in real time. The blockchain network adopts a Hyperledger architecture, with a consensus cluster composed of university nodes, recruitment platform nodes, and alumni company nodes. When an alumnus updates their LinkedIn profile with "promotion to head of ByteDance's algorithm team," the record, after verification via company email, generates a block containing a timestamp, data hash, and digital signature. On-chain storage supports tracing their career trajectory along a timeline. The dynamic update mechanism uses WeChat to periodically push intelligent questionnaires, requiring alumni to supplement project experience details quarterly. The NLP module uses a BiLSTM-CRF model to extract technical entities and achievement indicators, automatically populating a standardized field library.

[0042] The dynamic time window model processes employment data by weighting it according to its timeliness: the weight of data from the most recent month is set to 0.9, and the weight decreases by 0.1 for each additional month. The weight of historical data exceeding 12 months is reset to zero. The model uses a path clustering algorithm to analyze the career advancement trajectories of alumni in the same major, extracts typical development patterns, and constructs a predictive model library to predict future skills demand gaps. It also generates early warning signals by combining alumni skills update data. The model automatically adjusts the built-in parameters of the predictive model library for different career paths based on industry fluctuations.

[0043] During peak recruitment season in the fintech industry, the system's dynamic time window model assigns a weight of 0.9 to quantitative trading positions newly added in the past month, a weight of 0.6 to blockchain development positions from three months ago, and a weight of zero to traditional bank IT positions from over 12 months ago. A path clustering algorithm analyzes 10 years of career data from 50 computer science alumni, identifying three patterns: technically specialized, management transition, and cross-industry. When building a predictive model based on these patterns, it detects a 120% year-on-year increase in demand for artificial intelligence positions, triggering a skills gap warning. When the education industry experiences a 30% reduction in positions due to policy impacts, the model automatically lowers the recommended priority for online education career paths and adjusts the correlation parameter from 0.7 to 0.5.

[0044] When constructing the alumni relationship graph, we analyze the promotion rate of alumni positions in the hierarchical dimension to identify benchmark nodes for rapid promotion; we draw industry penetration heat maps in the industry dimension to detect abrupt changes in penetration rate in emerging fields; we mark the alumni concentration in a region in the geographical dimension to identify alumni resource-rich areas in the region; we quantify the influence of alumni nodes through the PageRank algorithm, screen key alumni and mark their associated corporate resources.

[0045] Analysis of the alumni network of the Computer Science Department at University A revealed that alumnus Zhang rose from P5 to P8 within three years, exceeding the promotion rate of 98% of similar nodes, thus being marked as a benchmark for rapid promotion. Industry-level heatmaps showed that the alumni penetration rate in the intelligent driving field surged from 5% to 18%, identifying companies A and B as emerging clusters. Geographic clustering revealed that the alumni density in region A reached 35 people / km. 2 This creates a resource-rich area. The improved PageRank algorithm incorporates industry popularity weights and promotion acceleration factors, calculating the influence value of alumnus Li to be 0.92. It then identifies the referral channels of autonomous driving companies associated with Li and generates key recommendation nodes.

[0046] After constructing a four-dimensional target model, a reinforcement learning mechanism is deployed to automatically reduce the weight of similar recommendations when a user marks a position as "not interested," and to reinforce the association features of successful job placement cases. A cold-start mechanism is designed to match lower-year students with alumni from the same department as career mentors. When Wang, a third-year student with a GPA of 3.6 and a research focus on computer vision, repeatedly marked "not interested" in recommended management positions, the reinforcement learning agent reduced the weight of the management-related feature from 0.5 to 0.2. Simultaneously, it monitored the successful cases of his classmates joining SenseTime, and reinforced the weight coefficients of features such as "winning a CV algorithm competition" and "publishing a top conference paper." For Chen, a first-year student, the cold-start mechanism matches him with Liu, a 2019 alumnus, generating an initial recommendation scheme based on departmental similarity, and gradually introducing cross-domain paths as user behavior data accumulates.

[0047] The federated learning framework constructs an encrypted feature alignment matrix to map the academic features of the school database and the career features of the recruitment platform to the same latent space. In the latent space, a differential privacy protection strategy is used to add Laplace noise to the association strength data of alumni relationship chains to ensure that individual career trajectories are irreversible.

[0048] When constructing the cross-domain model, the encrypted feature alignment matrix maps academic features within the university, such as "A grade in Data Structures," to career features on recruitment platforms, such as "proficiency in Kubernetes," into a 256-dimensional latent space, eliminating feature redundancy through orthogonal projection. When calculating alumni relationship strength, Laplacian noise with ε = 0.1 is added to the data on "Alumni A and B's activity level in the same technical community" to ensure that individual career trajectory details cannot be inferred from the association strength. The federated aggregation server only receives encrypted gradient parameters; local data is always retained on the original nodes.

[0049] In the digital twin career model, a benefit table is established by quantifying time opportunity costs and expected salary differences. Based on alumni's career data in industry and related fields, the system predicts promotion probabilities, skill enhancement potential, and industry risk coefficients to form analytical data. The benefit table and analytical data are then imported into an interactive device for visualization. To quantify the differences in career choices, the system calculates the expected 10-year benefits of path A (domestic internet giants) and path B (overseas research institutions): Path A has lower time opportunity costs, requiring only 5 years of experience to reach P8, but the maximum salary difference is 2 million RMB; Path B has lower initial salaries but greater skill enhancement potential. The digital twin model, combined with data from 200 alumni, predicts a 65% promotion probability for path A (45% for path B), but an industry risk coefficient (internet layoff index) of 0.3. Through HoloLens devices, students can interactively view a 3D career development tree—selecting path A displays a virtual scene of Alibaba's Hangzhou campus and skill requirements, while selecting path B presents the MIT lab environment and paper publication requirements, assisting them in developing long-term plans.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A career development recommendation method based on alumni relationship chain and dynamic employment data, characterized in that, The method comprises the following steps: Integrate academic data of the on-campus student database with dynamic employment data of the external recruitment platform, realize credible storage and update of the alumni career track through blockchain technology, analyze unstructured text and extract standardized fields using natural language processing technology; Build a dynamic time window model, process employment information by hierarchical weighting according to data timeliness, analyze the career transition track of alumni of the same major using path clustering algorithm, and generate three typical development mode libraries: technology deepening type, management transformation type and cross-industry transition type; Construct an alumni relationship graph from the dimensions of hierarchy, industry and geography, analyze the position promotion rate and regional aggregation characteristics, identify key referral nodes and label associated enterprise resources through an improved PageRank algorithm; Based on the four-dimensional target model of job matching degree, alumni correlation strength, development potential and salary growth space, deploy a reinforcement learning mechanism to dynamically adjust the recommendation weight, and match low-grade students with fast-promotion alumni as career mentors; Use the federated learning framework to realize cross-domain data collaborative modeling, add noise protection to the relationship chain analysis results to protect privacy, generate a digital twin career body to simulate different development paths, and display them through interactive devices; and evaluate the matching data quality and update frequency through the contribution point algorithm designed in the modeling; active alumni are given points according to the data quality evaluation module detecting alumni information update frequency and field integrity; based on smart contracts, exchange referral opportunities and consulting services for contribution points, and record the whole process of point transfer and generate tamper-proof evidence; Real-time monitoring of associated industry fluctuations and job competition index, setting a time period to trigger alternative path recommendations and update prediction model parameters, forming a closed-loop optimization mechanism, and the prediction model parameters predict future skill demand gaps.

2. The career development recommendation method based on alumni relationship chain and dynamic employment data according to claim 1, characterized in that: When integrating the academic data of the on-campus student database with the dynamic employment data of the external recruitment platform, the student database is connected to obtain student information, course grades, research projects and competition award data, and the external recruitment platform API is accessed in real time to collect alumni position changes and skill certification update data; a blockchain evidence network is built to store alumni career track data in a time-stamped chain, ensuring that the data cannot be tampered with and supporting historical version tracing; a dynamic data update mechanism is designed to trigger alumni career information supplementation through an intelligent questionnaire system regularly, and unstructured text descriptions are analyzed and standardized fields are extracted using natural language processing technology.

3. The method for career development recommendation based on alumni relationship chain and dynamic employment data according to claim 1, characterized in that: The dynamic time window model processes employment data by hierarchical weighting according to timeliness: the weight of data within 1 month is set to 0.9, the weight decays by 0.1 for each additional month, and the weight of historical data exceeding 12 months is zero; the path clustering algorithm is used to analyze the career transition track of alumni of the same major, extract typical development patterns and build a prediction model library to predict future skill demand gaps, generate early warning signals combined with alumni skill update data; automatically correct the built-in parameters of the prediction model library for different career paths according to industry fluctuations.

4. The method for career development recommendation based on alumni relationship chain and dynamic employment data according to claim 1, characterized in that: In the construction of alumni relationship map, the promotion rate of alumni positions is analyzed in the hierarchical dimension to identify fast promotion benchmark nodes; the industry penetration heat map is drawn in the industry dimension to detect the penetration rate mutation point in emerging fields; the regional alumni concentration is marked in the geographical dimension to identify the regional alumni resource enrichment area; the influence of alumni nodes is quantified by PageRank algorithm to screen key alumni and mark their associated enterprise resources.

5. The method for career development recommendation based on alumni relationship chain and dynamic employment data according to claim 1, characterized in that: After building the four-dimensional target model, through the deployment of reinforcement learning mechanism, when the user marks "not interested", the weight of similar recommendations is automatically reduced, and the associated features of successful employment cases are enhanced in the opposite direction; a cold start solution mechanism is designed to match low-grade students with alumni from the same department as career mentors.

6. The method for career development recommendation based on alumni relationship chain and dynamic employment data according to claim 1, characterized in that: The federated learning framework maps the academic features of the school database and the professional features of the recruitment platform to the same latent space by constructing an encrypted feature alignment matrix, and adds Laplace noise to the correlation strength data of the alumni relationship chain in the latent space through a differential privacy protection strategy to ensure that the individual career trajectory is irreversible.

7. The method for career development recommendation based on alumni relationship chain and dynamic employment data according to claim 1, characterized in that: In the digital twin career body, a benefit table is established by quantifying the time opportunity cost and expected salary difference, the analysis data of job promotion probability, skill value-added space and industry risk coefficient are formed according to the career data of alumni in industry and related fields, and the benefit table and analysis data are imported into the interactive device for visual display.

Images