Salary Range Engine Using ML Profile Correlation
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Solution Overview
Problem
Professional social networking services lack an efficient method to infer accurate salary ranges for members based on their profile attributes, relying on incomplete or outdated data.
Innovation Solution
A salary range engine correlates member profile attributes with trained salary data using machine learning algorithms, incorporating ratings and endorsements from other members to infer a target salary range, which is then used to update the training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine salary ranges, then the process is simple and requires minimal data processing, but the accuracy and relevance of salary estimates are insufficient
Solution Approach 1:
The system performs preliminary actions by pre-processing member profile data to extract relevant attributes (industry, job title, location, seniority) and pre-training the machine learning model with historical salary data. This preparation enables accurate salary estimation without requiring complex real-time processing during actual salary determination.
Solution Approach 2:
The patent replaces traditional mechanical/data-processing approaches with machine learning algorithms that automatically learn patterns from historical salary data. The neural network or regression models substitute for manual data analysis and processing, enabling more accurate predictions while reducing the complexity of human intervention in the process.
2Measurement precision
If more member profile attributes are considered in salary inference, then the accuracy of salary ranges improves, but the complexity of data processing increases
Solution Approach 1:
The system segments the complex task of salary estimation by dividing member profiles into distinct attributes (industry, job title, location, seniority). Each attribute is processed separately through the machine learning model, allowing the system to handle multiple factors independently and reduce the overall complexity of processing all attributes simultaneously.
Solution Approach 2:
The patent applies parameter changes by transforming raw profile data into standardized attribute formats that the machine learning model can process effectively. The system adjusts and normalizes different types of data (text, numeric, categorical) into consistent parameters, enabling accurate processing of multiple attributes without increasing computational complexity.
3Adaptability or versatility
If salary data is updated dynamically based on new information, then the relevance of salary estimates improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary training with historical salary data to establish baseline models. When new information becomes available, the system can quickly update the model parameters rather than retraining from scratch, maintaining data freshness while minimizing processing time through the pre-established model architecture.
Solution Approach 2:
The patent implements feedback mechanisms where new salary information and member profile updates are fed back into the machine learning model to continuously refine predictions. This feedback loop allows the system to adapt to changing market conditions while the automated nature of the process minimizes manual intervention time.
Data Source
AI summary
Systems, methods and a machine-readable media are described herein for a salary range engine to identify at least one attribute of a first member profile from a plurality of member profiles of a social networking service. The salary range engine correlates the at least one attribute with respect to at least a portion of trained salary data in a trained salary data repository. The salary range engine infers a target salary range based on a correlation between the at least one attribute of the first member profile and at least the portion of the trained salary data.