Offre en lien avec l’Action/le Réseau : DOING/– — –
Laboratoire/Entreprise : SAMOVAR/Télécom SudParis
Durée : 6 mois
Contact : romerojulien34@gmail.com
Date limite de publication : 2024-02-28
Contexte :
Job recommendation is the task of associating candidates with
jobs. This can be useful for candidates who would like to find to best possible
jobs, for companies that want to find the rarest talents in the vast pool of
candidates, but also for independent recruiters who need to be as precise as
possible when they send a resume to a company.
In this internship, you will work on a new dataset for job recommendations.
Its particularity is that it contains much additional information about candi-
dates and jobs we can represent as a graph. Besides, it is very sensitive to the
cold start problem: We have many new candidates and new jobs, and it restricts
a lot of the algorithms we can use.
If we consider video recommendations on Youtube, an average viewer watches
many videos, and each video is viewed many times. Therefore, when recom-
mending new videos to a specific user, we can look at what other similar view-
ers watched and recommend the most relevant video. This is the principle of
collaborative filtering. In our case, our users are likely to get a job and never
come back. Likewise, jobs are associated with one person, and then, we are
done with it. Therefore, we need to exploit extra information to make the
recommendation.
For our dataset, we can represent our pool of candidates and jobs with a
heterogeneous graph, connecting candidates and jobs, but also additional node
types like skills, cities, or employment types. Because we have this expressive
representation, we must adapt the existing algorithms. During the internship,
we will see how graph neural networks can be used to make recommendations,
and we will propose a new architecture to solve our specific problem.
The goal of this internship will be to publish a paper at an international
conference. The intern will work together with a Ph.D. student.
Sujet :
Job recommendation is the task of associating candidates with
jobs. This can be useful for candidates who would like to find to best possible
jobs, for companies that want to find the rarest talents in the vast pool of
candidates, but also for independent recruiters who need to be as precise as
possible when they send a resume to a company.
In this internship, you will work on a new dataset for job recommendations.
Its particularity is that it contains much additional information about candi-
dates and jobs we can represent as a graph. Besides, it is very sensitive to the
cold start problem: We have many new candidates and new jobs, and it restricts
a lot of the algorithms we can use.
If we consider video recommendations on Youtube, an average viewer watches
many videos, and each video is viewed many times. Therefore, when recom-
mending new videos to a specific user, we can look at what other similar view-
ers watched and recommend the most relevant video. This is the principle of
collaborative filtering. In our case, our users are likely to get a job and never
come back. Likewise, jobs are associated with one person, and then, we are
done with it. Therefore, we need to exploit extra information to make the
recommendation.
For our dataset, we can represent our pool of candidates and jobs with a
heterogeneous graph, connecting candidates and jobs, but also additional node
types like skills, cities, or employment types. Because we have this expressive
representation, we must adapt the existing algorithms. During the internship,
we will see how graph neural networks can be used to make recommendations,
and we will propose a new architecture to solve our specific problem.
The goal of this internship will be to publish a paper at an international
conference. The intern will work together with a Ph.D. student.
Profil du candidat :
The intern should be involved in a master’s program and have
a good knowledge of machine learning, deep learning, natural language processing, and graphs. A good understanding of Python and the standard libraries
used in data science (scikit-learn, PyTorch, pandas, transformers) is also expected. In addition, a previous experience with graph neural networks would be appreciated.
Formation et compétences requises :
The intern should be involved in a master’s program and have
a good knowledge of machine learning, deep learning, natural language processing, and graphs. A good understanding of Python and the standard libraries
used in data science (scikit-learn, PyTorch, pandas, transformers) is also expected. In addition, a previous experience with graph neural networks would be appreciated.
Adresse d’emploi :
Télécom Sudparis, Palaiseau
Document attaché : 202312181300_internship_job_recommandation-2.pdf