Machine Learning Job Board Recommendations From Multi-Step Hiring Data
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Solution Overview
Problem
Selecting an appropriate set of job boards to transmit a job requisition is challenging due to varying characteristics among job boards, leading to unnecessary fees and increased management overhead.
Innovation Solution
A machine learning model is trained to generate probabilities for posting platforms based on numerical representations of prior posts, candidate progression through recruiting processes, and indicators of successful outcomes, using vectorization and supervised learning algorithms to recommend optimal platforms for new job postings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a job requisition is transmitted to multiple job boards to increase candidate attraction, then the number of potential candidates increases, but fees and management overhead increase
Solution Approach 1:
The system changes the parameter of job board selection from manual or random choice to data-driven selection based on multiple characteristics including demographic distribution, fee schedules, search algorithms, and candidate intake processes. This allows optimization of the candidate attraction efficiency while controlling costs and management complexity.
Solution Approach 2:
The system implements feedback mechanisms by analyzing historical posting data, candidate response rates, and recruitment outcomes from different job boards. This feedback is used to continuously refine the selection algorithm, improving candidate attraction while reducing unnecessary postings to ineffective platforms.
2Quantity of substance
If a job requisition is transmitted to multiple job boards to increase candidate attraction, then the number of potential candidates increases, but fees increase
Solution Approach 1:
The system incorporates fee schedules as a key parameter in the selection algorithm, transforming the approach from indiscriminate posting to cost-conscious selection. By evaluating job boards based on their fee structures combined with their effectiveness metrics, the system optimizes the ratio of candidate attraction to spending.
Solution Approach 2:
Rather than posting to all available job boards (excessive action), the system selectively posts to a optimized subset of platforms that provide the best return on investment. This partial action approach achieves sufficient candidate attraction while minimizing unnecessary fees.
3Adaptability or versatility
If job boards with different characteristics are used to attract diverse candidates, then candidate diversity increases, but selection difficulty increases
Solution Approach 1:
The system applies local quality by matching specific job requisition characteristics with corresponding job board characteristics. Different job types, locations, and requirements are routed to job boards with demonstrated effectiveness for those specific categories, rather than using a one-size-fits-all approach. This reduces selection difficulty by creating targeted matching rules.
Solution Approach 2:
The system transforms the complex selection problem into a parameter-based matching process, where job requisition parameters (role, location, industry, etc.) are systematically compared against job board parameters (demographic distribution, user base characteristics, etc.). This parameterization simplifies the detection and measurement of suitable platforms.
Data Source
AI summary
Systems and methods include reception of a first post comprising text, identification of one or more prior posts similar to the first post from a plurality of prior posts based on the text of the first post and text of the plurality of prior posts, determination, for each identified one or more prior posts, of probabilities associated with each of two or more posting platforms, for each of the identified one or more posts, determination of a weighted probability associated with each of two or more posting platforms based on a similarity of the post to the first post and the probabilities associated with each of two or more posting platforms for the post, determination of one or more of the two or more posting platforms based on the weighted probabilities, and transmission of the first post to the determined one or more posting platforms.