Academic Publications

@conference{duperis2022,

title = {Deep Unordered Composition for Multi-label Classification applied to Skills Prediction},

author = {Yann Duperis and Adrian-Gabriel Chifu and Bernard Espinasse and Sébastien Fournier and Arthur Kuehn},

url = {http://ceur-ws.org/Vol-3178/CIRCLE_2022_paper_16.pdf},

year  = {2022},

date = {2022-07-04},

urldate = {2022-07-04},

booktitle = {Joint Conference of the Information Retrieval Communities in Europe CIRCLE 2022},

address = {Samatan, France},

abstract = {Today, many recruitment processes are digitalized. Job offers are posted on job boards and candidates apply by submitting their resumes. To select an appropriate candidate for a job, recruiters rely mostly on the evaluation of the professional skills of the individual. However, researches have shown that individuals tend to omit some skills from their professional profile. A human recruiter, knowledgeable in a given activity sector, is often able to fill the gaps and infer the missing skills. In this paper our aim is to support this human recruiter by automatically inferring theses missing skills, a non-trivial task. To solve this task, first we propose a method to tackle the skill prediction problem by transforming it from a multi-label classification task it to a binary classification task. Then we implement this method with a deep learning model inspired by the Deep Unordered Composition approach. Two different variants of this model, one with the Deep Averaging Network architecture and the other with the Set-Transformer architecture, are evaluated on an open IT resumes data set, and the results are promising.},

keywords = {Job recommender system, Natural Language Processing, Neural Networks},

pubstate = {published},

tppubtype = {conference}

}

@conference{Duperis2021,

title = {Vers un système de recommandation de profils experts dans l'industrie des procédés},

author = {Yann Duperis and Adrian-Gabriel Chifu and Bernard Espinasse and Sébastien Fournier and Arthur Kuehn},

url = {http://coria.asso-aria.org/2021/articles/long_12/main.pdf},

year  = {2021},

date = {2021-04-15},

urldate = {2021-04-15},

booktitle = {COnférence en Recherche d’Information et Applications},

address = {Grenoble, France (virtuel)},

series = {CORIA2021},

abstract = {La dématérialisation des processus de recrutement n'a pas fait disparaître toutes les frictions inhérentes à cette activité. La recherche automatisée d'un candidat idéal se heurte toujours à la difficulté à modéliser correctement les besoins exprimés en langage naturel dans une offre d’emploi. Le recrutement d’experts, notamment, est particulièrement difficile. En effet, ces profils concernent une proportion réduite des recrutements et leur prise en charge informatisée nécessite une connaissance précise du secteur d’activité concerné. Dans cet article, nous proposons l’architecture d’un système de recommandation de profils experts dans l’industrie des procédés afin d’assister ce type de recrutements.},

keywords = {Expert search, Job recommender system, Semantic web},

pubstate = {published},

tppubtype = {conference}

}