Inverse Propensity Weighting for Position Bias in Candidate Matching
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Solution Overview
Problem
Machine-learned models used in recruiter-candidate matching face position bias due to training data that favors candidates presented higher in the list, leading to inaccurate scores and resource wastage, as recruiters are more likely to contact higher-ranked candidates.
Innovation Solution
Introducing an inverse propensity weight into the loss function to increase the weight of candidates presented lower in the list, counteracting position bias by generating scores that reflect the likelihood of recruiter interest and candidate response, with separate functions for the first preset number of candidates and those beyond.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learned models use training data from actual recruiter-candidate interactions, then the model can learn real-world patterns, but position bias is introduced where higher-ranked candidates are favored
Solution Approach 1:
The patent applies counterweight by introducing inverse propensity scores that act as compensating forces against position bias. These scores are calculated based on the position in the candidate list and are used to re-weight the training data, effectively counterbalancing the inherent favoring of higher-ranked candidates and creating a more公平 evaluation system
Solution Approach 2:
The patent changes the parameter of data weighting by transforming the uniform treatment of training samples into position-aware weighted sampling. By modifying how training data is weighted based on candidate position, the model learns to account for position bias rather than being biased by it, improving both scoring accuracy and fairness
2Manufacturing precision
If recruiters contact more candidates to improve match quality, then better candidates may be found, but time and resources are wasted on candidates unlikely to respond
Solution Approach 1:
The patent applies preliminary action by having the machine-learned model predict candidate response probability before recruiters spend time contacting candidates. This pre-screening allows recruiters to prioritize candidates who are most likely to respond, performing the useful action of filtering in advance to avoid wasting time on unlikely respondents
Solution Approach 2:
The system implements feedback by using actual candidate response outcomes to continuously retrain and improve the machine-learned model. The model learns from past interactions which candidate characteristics and positions correlate with responses, refining its predictions over time to better guide recruiter efforts and reduce wasted time
3Productivity
If recruiters focus on higher-ranked candidates due to position bias, then contact efficiency may improve, but accurate identification of truly qualified candidates is reduced
Solution Approach 1:
The patent applies segmentation by dividing the candidate evaluation into two independent scoring components: the machine-learned model's intrinsic quality score and the position bias adjustment score. This segmentation allows the system to separately evaluate candidate merit and position effects, preventing position bias from conflating with true quality assessment and enabling more accurate identification of qualified candidates
Data Source
AI summary
In an example embodiment, position bias is addressed by introducing an inverse propensity weight into a loss function used to train a machine-learned model. This inverse propensity weight essentially increases the weight of candidates in the training data that were presented lower in a list of candidates. This achieves the benefit of counteracting the position bias and increases the effectiveness of the machine-learned model in generating scores for future candidates. In a further example embodiment, a function is generated for the inverse propensity weight based on responses to contact requests from recruiters. In other words, while the machine learned-model may factor in both the likelihood that a recruiter will want to contact a candidate and the likelihood that a candidate will respond to such a contact, the function generated for the inverse propensity weight will be based only on training data where the candidate actually responded to a contact.