Chargement...
 

Machine Learning-Robotics

Domaine
Machine Learning-Robotics
Domain - extra
Année
2014
Starting
Sept. 2014
État
Open
Sujet
A Collaborative Filtering Approach to Matching Job Openings and Job Seekers

Thesis advisor
SEBAG Michèle
Co-advisors
Marc Schoenauer
Laboratory
Collaborations
Abstract
Digital commerce, with endless catalogs of items, can only thrive through recommendation engines,
suggesting likable items to a user based on the items she liked in the past, or based on items liked
by other users, with similar tastes. recommending appropriate ite
Many approaches, including collaborative filtering, have been developed to recommend items to users, based on the items they bought (and probably liked) previously, or based on the items that others users liked.
Collaborative filtering is one major approach behind recommenders, mapping items and users in the
same so-called latent space.

The PhD topic aims at leveraging collaborative filtering to tackle the social problem of
mismatch between job openings and job seekers.

Context
Objectives
Work program
Extra information
Prerequisite
Détails
Expected funding
Institutional funding
Status of funding
Expected
Candidates
Utilisateur
michele-martine.sebag
Créé
Mardi 17 juin 2014 17:46:06 CEST
dernière modif.
Mardi 17 juin 2014 17:46:06 CEST

Fichiers joints

 filenamecrééhitsfilesize 
Aucun fichier joint à cette fiche


Ecole Doctorale Informatique Paris-Sud


Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps

ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr