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AssigneeMicrosoft Technology Licensing, LLC
InventorsDan Liu, Daniel Sairom Krishnan Hewlett, Qi Guo
Estimating the term…
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.
A system for returning search results in an online computer system, the system comprising:
A computer-implemented method for returning search results in an online computer system, the method comprising:
A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations comprising:
Dependencies are inferred from the claim text.