The Influence of Feature Selection on Job Clustering for an E-recruitment Recommender System

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dc.contributor.authorJunior J.J.S.
dc.contributor.authorVilasboas F.G.
dc.contributor.authorde Castro L.N.
dc.date.accessioned2024-03-12T23:49:30Z
dc.date.available2024-03-12T23:49:30Z
dc.date.issued2020
dc.description.abstract© 2020, Springer Nature Switzerland AG.Recommender systems aim to effectively recommend items to the user based on their profile. An online recruitment system recommends jobs for a candidate according to his profile and can also act in reverse, recommending more qualified candidates for a particular job. Defining which variables will be used impacts directly the recommendation quality so that, when using the most important variables, we have a better assertiveness in the process. The goal of this work is to select the most important features of an online recruitment database using feature selection techniques. More specifically, we used the algorithms of Mitra, SUD and ACA to perform feature selection. The datasets used were derived from the original dataset assuming three distinct scenarios: the dataset containing the attributes related with the jobs' features; the dataset containing the bag of words of the description feature of the jobs; and the dataset resulting from the union of the two previous ones. The features' subsets selected in each of the above scenarios had their performance evaluated in a clustering task. The results obtained in each scenario show a performance gain of the clustering process when feature selection is made over the original data. Also, it was observed that the jobs' features result in better performance than the other two cases.
dc.description.firstpage176
dc.description.lastpage187
dc.description.volume12416 LNAI
dc.identifier.doi10.1007/978-3-030-61534-5_16
dc.identifier.issn1611-3349
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/35066
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsAcesso Restrito
dc.subject.otherlanguageE-recruitment
dc.subject.otherlanguageFeature selection
dc.subject.otherlanguageRecommender system
dc.titleThe Influence of Feature Selection on Job Clustering for an E-recruitment Recommender System
dc.typeArtigo de evento
local.scopus.citations3
local.scopus.eid2-s2.0-85096540800
local.scopus.subjectBag of words
local.scopus.subjectClustering process
local.scopus.subjectImportant features
local.scopus.subjectOnline recruitment
local.scopus.subjectPerformance Gain
local.scopus.subjectSelection techniques
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85096540800&origin=inward
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