[Show/Hide Left Column]

Sujets Help

View Item

Domain Machine Learning-Robotics
Domain - extra
Year 2014
Starting Sept. 2014
Status Open
Subject A Collaborative Filtering Approach to Matching Job Openings and Job Seekers

Thesis advisor SEBAG Michèle
Co-advisors Marc Schoenauer
Laboratory LRI A&O
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.

Work program
Extra information
Expected funding Institutional funding
Status of funding Expected
user michele-martine.sebag
Created Tuesday 17 of June, 2014 17:46:06 CEST
LastModif Tuesday 17 of June, 2014 17:46:06 CEST

Ecole Doctorale Informatique Paris-Sud

Nicole Bidoit
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 at lri.fr