Abstract: Systems, methods, and non-transitory computer readable media are configured to obtain a first identifier and a second identifier for at least one entity constituting potential features to train a machine learning model. The first identifier and the second identifier are applied to an embedding model for generating vector representations in a vector space associated with a desired feature dimensionality. A first vector representation associated with the first identifier and a second vector representation associated with the second identifier are applied as features to train the machine learning model.

Abstract: Systems, methods, and non-transitory computer readable media are configured to generate a relevance score for each resume of a plurality of resumes associated with job candidates based on one or more machine learning models in a first stage associated with a job pipeline of an organization, the relevance score indicative of relevance of the resume in relation to the job pipeline. A subset of resumes are selected from the plurality of resumes, the subset of resumes having highest relevance scores. A quality score for each selected resume of the subset of resumes is generated based on a machine learning model in a second stage associated with the job pipeline, the quality score indicative of quality of the selected resume in relation to the job pipeline.

Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a first score generated by a first scoring algorithm that determines a degree to which a resume is matched to a job pipeline of an organization. A second score generated by a second scoring algorithm that determines a degree to which the resume is matched to the job pipeline is determined. The first score and the second score are processed to generate an aggregate score.