Gilbert Saporta - A Semi-Supervised Recommender System to Predict Online Job Offer Performance

11:00
Jeudi
9
Fév
2012
Organisé par : 

Gilles Bisson, équipe AMA

Intervenant : 

Gilbert Saporta (CNAM)

Équipes : 

Information détaillée : 

ANNONCE DE SÉMINAIRE DE L’ÉQUIPE AMA

Jeudi 9 février 11H00 : "A Semi-Supervised Recommender System to Predict Online Job Offer Performance

Invited speaker : Gilbert Saporta (CNAM)

Lieu des séminaires AMA : 
Grande salle de réunion TURING 
Centre Equation 4 
ZI DE MAYENCIN 
Allée de la Palestine - 38610 GIERES

Résumé : 

Job vacancies are more and more published exclusively on internet . In order to predict job posting performance on a job board website , a Ph.D. sponsored by Multiposting company is prepared by J.Séguéla. An intelligent tool is developed, which recommends the best job boards according to the job offer. The system is based on the analysis of both structured and unstructured (textual) characteristics of previous job offers. We present here a semi-supervised recommender system predicting the ranking of job boards with respect to job posting returns and compare it with other supervised and non supervised approaches.

Keywords : Statistical methodology includes Latent Semantic Indexing, PLS regression and k-nn prediction.