SYSTEMS AND METHODS FOR AUTOMATED CANDIDATE RECOMMENDATIONS
Systems, methods, and non-transitory computer-readable media can generate a relevance score for each candidate of a plurality of candidates based on a relevance model. The relevance score is indicative of a relevance of the candidate in relation to a talent pipeline. A quality score is generated for each candidate of the plurality of candidates based on a quality model. The quality score is indicative of a likelihood of the candidate to receive a job offer if the candidate is interviewed. A candidate score is generated for each candidate of the plurality of candidates based on the relevance score and the quality score.
1 . A computer-implemented method comprising:
generating, by a computing system, a relevance score for each candidate of a plurality of candidates based on a relevance model, the relevance score indicative of a relevance of the candidate in relation to a talent pipeline;
generating, by the computing system, a quality score for each candidate of the plurality of candidates based on a quality model, the quality score indicative of a likelihood of the candidate to receive a job offer if the candidate is interviewed; and
generating, by the computing system, a candidate score for each candidate of the plurality of candidates based on the relevance score and the quality score.
2 . The computer-implemented method of claim 1 , further comprising ranking at least a subset of the plurality of candidates based on candidate score.
3 . The computer-implemented method of claim 2 , further comprising providing a ranked list of candidates based on the ranking at least the subset of the plurality of candidates based on candidate score.
4 . The computer-implemented method of claim 3 , wherein the ranked list excludes one or more candidates of the plurality of candidates that do not satisfy a minimum relevance score threshold.
5 . The computer-implemented method of claim 1 , wherein
the relevance model is a first relevance model of a plurality of relevance models,
the talent pipeline is a first talent pipeline of a plurality of talent pipelines; and
the first relevance model is associated with the first talent pipeline.
6 . The computer-implemented method of claim 5 , wherein the first relevance model is trained based on a first set of training data associated with a first plurality of previous candidates.
7 . The computer-implemented method of claim 6 , wherein the relevance model is trained based on a binary label for each candidate of the first plurality of previous candidates indicative of whether the candidate was claimed by a recruiter associated with the first talent pipeline.
8 . The computer-implemented method of claim 7 , wherein the quality model is trained based on a second set of training data associated with a second plurality of previous candidates.
9 . The computer-implemented method of claim 8 , wherein
the first plurality of previous candidates are associated with the first talent pipeline; and
the second plurality of previous candidates are associated with the plurality of talent pipelines.
10 . The computer-implemented method of claim 1 , wherein
the quality model comprises a first sub-model configured to output a first quality sub-score,
the quality model comprises a second sub-model configured to output a second quality sub-score,
the quality score is generated based on the first quality sub-score and the second quality sub-score,
the first sub-model is trained based on a first binary label for each candidate of a first plurality of previous candidates indicative of whether the candidate was invited to participate in a second round of interviews, and
the second sub-model is trained based on a second binary label for each candidate of a second plurality of previous candidates indicative of whether the candidate was extended a job offer.
11 . A system comprising:
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the system to perform a method comprising:
generating a relevance score for each candidate of a plurality of candidates based on a relevance model, the relevance score indicative of a relevance of the candidate in relation to a talent pipeline;
generating a quality score for each candidate of the plurality of candidates based on a quality model, the quality score indicative of a likelihood of the candidate to receive a job offer if the candidate is interviewed; and
generating a candidate score for each candidate of the plurality of candidates based on the relevance score and the quality score.
12 . The system of claim 11 , wherein the instructions, when executed by the at least one processor, further cause the system to perform: ranking at least a subset of the plurality of candidates based on candidate score.
13 . The system of claim 12 , wherein the instructions, when executed by the at least one processor, further cause the system to perform: providing a ranked list of candidates based on the ranking at least the subset of the plurality of candidates based on candidate score.
14 . The system of claim 13 , wherein the ranked list excludes one or more candidates of the plurality of candidates that do not satisfy a minimum relevance score threshold.
15 . The system of claim 11 , wherein
the relevance model is a first relevance model of a plurality of relevance models,
the talent pipeline is a first talent pipeline of a plurality of talent pipelines; and
the first relevance model is associated with the first talent pipeline.
16 . A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:
generating a relevance score for each candidate of a plurality of candidates based on a relevance model, the relevance score indicative of a relevance of the candidate in relation to a talent pipeline;
generating a quality score for each candidate of the plurality of candidates based on a quality model, the quality score indicative of a likelihood of the candidate to receive a job offer if the candidate is interviewed; and
generating a candidate score for each candidate of the plurality of candidates based on the relevance score and the quality score.
17 . The non-transitory computer-readable storage medium of claim 16 , wherein the instructions, when executed by the at least one processor, further cause the computing system to perform: ranking at least a subset of the plurality of candidates based on candidate score.
18 . The non-transitory computer-readable storage medium of claim 17 , wherein the instructions, when executed by the at least one processor, further cause the computing system to perform: providing a ranked list of candidates based on the ranking at least the subset of the plurality of candidates based on candidate score.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein the ranked list excludes one or more candidates of the plurality of candidates that do not satisfy a minimum relevance score threshold.
20 . The non-transitory computer-readable storage medium of claim 16 , wherein
the relevance model is a first relevance model of a plurality of relevance models,
the talent pipeline is a first talent pipeline of a plurality of talent pipelines; and
the first relevance model is associated with the first talent pipeline.