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Solal Nathan

Tomorrow we will be presenting our paper "Toward Job Recommendation for all" at in the AI for Social Good Track. It is about Job Recommender Systems.

Feel free to come and say hi and reach out if you have any questions.

Paper, Slides, Poster and Code available on my website: solalnathan.com/publications/

Title: Toward Job Recommendation for All Authors: Guillaume Bied1,2, Solal Nathan1, Elia Perennes2,3, Morgane Hoffmann2,3, Philippe Caillou1, Bruno Crépon2, Christophe Gaillac4 and Michele Sebag1 Affiliations: 1. Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Orsay, France 2.Centre de Recherche en Economie et Statistique (CREST), Palaiseau, France 3. pole emploi, Paris, France “Oxford University, 4. Oxford, United Kingdom bied@lri.fr
Abstract This paper presents a job recommendation algorithm designed and validated in the context of the French Public Employment Service. The challenges, owing to the confidential data policy, are related with the extreme sparsity of the interaction matrix and the mandatory scalability of the algorithm, aimed to deliver recommendations to millions of job seekers in quasi real-time, considering hundreds of thousands of job ads. The experimental validation of the approach shows similar or better performances than the state of the art in terms of recall, with a gain in inference time of 2 orders of magnitude. The study includes some fairness analysis of the recommendation algorithm. The gender-related gap is shown to be statistically similar in the true data and in the counter-factual data built from the recommendations.
The poster associated with the paper. A PDF version (which would be more legible for screen readers is available at https://solalnathan.com/publications/IJCAI_2023_poster.pdf)
#ai#ml#ijcai23
Aug 23, 2023, 02:57 PM·Public·Web
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