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Field
Abstract
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Before
I study the implementation of a job recommender system dedicated to the jobseekers at the French Public Employment Service (PES). The recommender system is a combination of two vacancy rankings : the first ranking is based on an expert system that recommends matches based on the fit between the job seekers' search criteria and the characteristics of job postings ; the other one is obtained from a state of the art machine learning (ML) model, based on the very rich data available at the French PES (including textual data and past hires).
The objectives of this study are threefold: first, it will measure the degree of aversion (and/or acceptance) of job seekers for algorithmic recommendations ; second, it will determine the mechanisms that cause aversion ; and third, it will investigate how the framing of recommendations can minimize the risk of aversion.
I set up a randomized controlled trial in which a set of job seekers is exposed to algorithmic recommendations of vacancies. Several framing alternatives are tested: the first explains to job seekers the global functioning of the algorithm, the second emphasizes the level of performance of the algorithm and the third emphasizes the fact that the algorithm has been co-constructed with job seekers. During the experiment, job seekers are explicitly asked about how they perceive the recommendations. I also use implicit satisfaction measures, such as the click rate on the recommended ads.
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After
We study the implementation of a job recommender system dedicated to the jobseekers at the French Public Employment Service (PES). The recommender system is a combination of two vacancy rankings : the first ranking is based on an expert system that recommends matches based on the fit between the job seekers' search criteria and the characteristics of job postings ; the other one is obtained from a state of the art machine learning (ML) model, based on the very rich data available at the French PES (including textual data and past hires).
The objectives of this study are threefold: first, it will measure the degree of aversion (and/or acceptance) of job seekers for algorithmic recommendations ; second, it will determine the mechanisms that cause aversion ; and third, it will investigate how the framing of recommendations can minimize the risk of aversion.
We set up a randomized controlled trial in which a set of job seekers is exposed to algorithmic recommendations of vacancies. Several framing alternatives are tested: the first explains to job seekers the global functioning of the algorithm, the second emphasizes the level of performance of the algorithm and the third emphasizes the fact that the algorithm has been co-constructed with job seekers. During the experiment, job seekers are explicitly asked about how they perceive the recommendations. I also use implicit satisfaction measures, such as the click rate on the recommended ads.
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