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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
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
Details
Expected funding Institutional funding
Status of funding Expected
Candidates
user michele-martine.sebag
Created Tuesday 17 of June, 2014 17:46:06 CEST
LastModif Tuesday 17 of June, 2014 17:46:06 CEST
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The original document is available at https://edips.lri.fr/tiki-view_tracker_item.php?itemId=4056