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Conference Papers Year : 2021

Designing labor market recommender systems: the importance of job seeker preferences and competition

Abstract

We examine the properties of a recommender algorithm currently under construction at the Public Employment Service (PES) in France, before its implementation in the field. The algorithm associates to each offer-job seeker pair a predicted "matching probability" using a very large set of covariates. We first compare this new AI algorithm with a matching tool mimicking the one currently used at the PES, based on a score measuring the "proximity" between the job seeker's profile or preference and the characteristics of the offer. We detail and discuss the trade-off between matching probability and preference score when switching from one system to the other. We also examine the issue of congestion. We show on the one hand that the AI algorithm tends to increase congestion and on the other hand that this strongly reduces its performance. We finally show that the use of optimal transport to derive recommendations from the matching probability matrix allows to mitigate this problem significantly. The main lesson at this stage is that an algorithm ignoring preferences and competition in the labor market would have very limited performances but that tweaking the algorithm to fit these dimensions improves substantially its properties, at least "in the lab".
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Dates and versions

hal-03540319 , version 1 (23-01-2022)

Identifiers

  • HAL Id : hal-03540319 , version 1

Cite

Victor Alfonso Naya, Guillaume Bied, Philippe Caillou, Bruno Crépon, Christophe Gaillac, et al.. Designing labor market recommender systems: the importance of job seeker preferences and competition. 4. IDSC of IZA Workshop: Matching Workers and Jobs Online - New Developments and Opportunities for Social Science and Practice, Oct 2021, Online, France. ⟨hal-03540319⟩
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