Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine a set of candidates based at least in part on filtering criteria. A subset of candidates can be determined from the set of candidates based at least in part on one or more recruiter features associated with a recruiter. A recommendation can be provided to the recruiter for a candidate from among the subset of candidates based at least in part on a ranking of the subset of candidates.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine one or more requisition clusters associated with a candidate, wherein the requisition clusters are associated with one or more requisitions. A requisition score associated with the one or more requisitions associated with the one or more requisition clusters can be determined based in part on the candidate. One or more requisition recommendations can be provided based in part on the requisition score.

Abstract: Systems, methods, and non-transitory computer readable media are configured to receive a resume corpus. A machine learning model is trained based on terms from the resume corpus. A job title for a user is determined based on profile information provided to the model.

Abstract: Systems, methods, and non-transitory computer readable media are configured to acquire a resume corpus. The resume corpus is processed to generate resume tokens. A machine learning model is trained based on the resume tokens. The machine learning model is applied to recommend a job classification based on evaluation data.

Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a feature set for a model to be trained by machine learning. A subset of features from the feature set can be associated with entities having relationship types and corresponding to pages on a social networking system. The feature set can be reduced based on at least one rule applied to the relationship types.

Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a training set to train a machine learning model. A feature set for the model is determined. The model is trained based on the training set and the feature set to determine a score reflecting a probability that each user in an evaluation set of users is qualified for employment with an organization. A ranking of users in the evaluation set is provided based on the score determined for each user.

Abstract: Systems, methods, and non-transitory computer readable media are configured to convert resume text in a resume into an array of values representing a frequency of keywords associated with the resume text. An array of values representing a frequency of search terms associated with a search is generated. The array of values representing a frequency of keywords associated with the resume text and the array of values representing a frequency of search terms associated with a search to generate a score for the resume are combined.

Abstract: 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.

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.

Abstract: Systems, methods, and non-transitory computer readable media are configured to determine scores regarding suitability of connections of a user for employment with an organization with which the user is employed based on a first machine learning model. Job titles for which the connections are suited are determined based on a second machine learning model. A user interface for presenting in real time information relating to the connections and associated job titles determined for the connections is generated.

Abstract: Systems, methods, and non-transitory computer readable media are configured to acquire a resume corpus. The resume corpus is processed to generate resume tokens. A machine learning model is trained based on the resume tokens. The machine learning model is applied to recommend a job classification based on evaluation data.

Abstract: Systems, methods, and non-transitory computer readable media are configured to receive a resume corpus. A machine learning model is trained based on terms from the resume corpus. A job title for a user is determined based on profile information provided to the model.

Abstract: Systems, methods, and non-transitory computer readable media are configured to convert resume text in a resume into an array of values representing a frequency of keywords associated with the resume text. An array of values representing a frequency of search terms associated with a search is generated. The array of values representing a frequency of keywords associated with the resume text and the array of values representing a frequency of search terms associated with a search to generate a score for the resume are combined.

Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a training set to train a machine learning model. A feature set for the model is determined. The model is trained based on the training set and the feature set to determine a score reflecting a probability that each user in an evaluation set of users is qualified for employment with an organization. A ranking of users in the evaluation set is provided based on the score determined for each user.

Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a feature set for a model to be trained by machine learning. A subset of features from the feature set can be associated with entities having relationship types and corresponding to pages on a social networking system. The feature set can be reduced based on at least one rule applied to the relationship types.