Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings

Detalhes bibliográficos
Autor(a) principal: Pombo, José Luís Fava de Matos
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/60405
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddingsJob matchingRecommendation systemsSemanticsMachine learningWord2vecTrabalho de projectoWork ProjectProject Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsJob application’ screening is a challenging and time-consuming task to execute manually. For recruiting companies such as Landing.Jobs it poses constraints on the ability to scale the business. Some systems have been built for assisting recruiters screening applications but they tend to overlook the challenges related with natural language. On the other side, most people nowadays specially in the IT-sector use the Internet to look for jobs, however, given the huge amount of job postings online, it can be complicated for a candidate to short-list the right ones for applying to. In this work we test a collection of Machine Learning algorithms and through the usage of cross-validation we calibrate the most important hyper-parameters of each algorithm. The learning algorithms attempt to learn what makes a successful match between candidate profile and job requirements using for training historical data of selected/reject applications in the screening phase. The features we use for building our models include the similarities between the job requirements and the candidate profile in dimensions such as skills, profession, location and a set of job features which intend to capture the experience level, salary expectations, among others. In a first set of experiments, our best results emerge from the application of the Multilayer Perceptron algorithm (also known as Feed-Forward Neural Networks). After this, we improve the skills-matching feature by applying techniques for semantically embedding required/offered skills in order to tackle problems such as synonyms and typos which artificially degrade the similarity between job profile and candidate profile and degrade the overall quality of the results. Through the usage of word2vec algorithm for embedding skills and Multilayer Perceptron to learn the overall matching we obtain our best results. We believe our results could be even further improved by extending the idea of semantic embedding to other features and by finding candidates with similar job preferences with the target candidate and building upon that a richer presentation of the candidate profile. We consider that the final model we present in this work can be deployed in production as a first-level tool for doing the heavy-lifting of screening all applications, then passing the top N matches for manual inspection. Also, the results of our model can be used to complement any recommendation system in place by simply running the model encoding the profile of all candidates in the database upon any new job opening and recommend the jobs to the candidates which yield higher matching probability.Vanneschi, LeonardoRUNPombo, José Luís Fava de Matos2019-02-13T17:03:59Z2019-01-292019-01-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/60405TID:202169359enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:28:52Zoai:run.unl.pt:10362/60405Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:33:30.463355Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings
title Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings
spellingShingle Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings
Pombo, José Luís Fava de Matos
Job matching
Recommendation systems
Semantics
Machine learning
Word2vec
Trabalho de projecto
Work Project
title_short Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings
title_full Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings
title_fullStr Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings
title_full_unstemmed Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings
title_sort Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings
author Pombo, José Luís Fava de Matos
author_facet Pombo, José Luís Fava de Matos
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Pombo, José Luís Fava de Matos
dc.subject.por.fl_str_mv Job matching
Recommendation systems
Semantics
Machine learning
Word2vec
Trabalho de projecto
Work Project
topic Job matching
Recommendation systems
Semantics
Machine learning
Word2vec
Trabalho de projecto
Work Project
description Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2019
dc.date.none.fl_str_mv 2019-02-13T17:03:59Z
2019-01-29
2019-01-29T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/60405
TID:202169359
url http://hdl.handle.net/10362/60405
identifier_str_mv TID:202169359
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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