Career Mapping via Machine Learning Feature Vectors
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Solution Overview
Problem
Current job recommendation platforms are limited in suggesting careers that users are not aware of, as they primarily match user-supplied data to user-supplied job descriptions without auto-generating job titles based on generic user data without user prompts, and fail to consider both professional and non-professional activities.
Innovation Solution
A method and system that processes raw user data to extract predefined content, transform it into feature vectors, and use machine learning models to predict careers by mapping professional and non-professional activities to specific character traits and industries, generating a range of potential careers with percentage matches, and dynamically updating predictions based on user profile changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If job recommendation platforms use user-supplied data to match user-supplied job descriptions, then the matching accuracy for known careers is improved, but the ability to recommend unknown careers automatically is limited
Solution Approach 1:
The system enables automatic career recommendation by allowing the platform to autonomously generate career suggestions based on user data without requiring users to explicitly search or specify desired careers. The machine learning model processes user profiles, skills, and experiences to self-generate relevant career recommendations, eliminating the need for users to actively search for job descriptions.
Solution Approach 2:
The system transforms the approach by changing from exact matching of user-supplied job descriptions to a probabilistic model that generates career recommendations based on multiple parameters including skills, experiences, education, and career trajectories. This parameter transformation enables the system to recommend careers beyond what users explicitly search for.
2Measurement precision
If platforms focus on matching user data to specific job titles, then the precision of job title matching is improved, but the breadth of career exploration is reduced
Solution Approach 1:
The system dynamically adjusts career recommendations based on user interactions, profile updates, and changing career landscapes. Rather than static matching to fixed job titles, the model continuously adapts to provide both precise current matches and exploratory recommendations for emerging or alternative career paths, balancing precision with breadth.
Solution Approach 2:
The system adds a new dimension to career recommendation by incorporating career trajectory analysis and skill progression pathways. This allows users to explore careers not just by current job titles but by potential future paths, skill development sequences, and industry evolution, thereby expanding career exploration breadth while maintaining matching precision.
3Measurement precision
If platforms only consider professional activities for career recommendations, then the relevance to current employment is improved, but the understanding of user potential is limited
Solution Approach 1:
The system universally processes multiple types of user data including professional experiences, educational background, personal projects, volunteer work, and skill certifications. This multi-functional data ingestion approach allows the model to comprehensively assess user potential beyond formal employment, capturing transferable skills and latent capabilities that may indicate suitability for different career paths.
Data Source
AI summary
A method for recommending a career to a user, the method comprising the steps of: at processing circuitry executing instructions stored in a memory device, receiving raw user data; preprocessing the raw user data; extracting predefined content from the user data; transforming text in the predefined content into frequency distribution and generating a predetermined number of feature vectors; generating a training data set and a test data set from the feature vector output; generating at least one model, and using the training data set and test data set to evaluate the performance of the at least one model; receiving subject user data; predicting the at least one career using the trained model on the subject user data; and generating a report comprising the at least one career.