Recommender system in a non-stationary context: recommending job ads in pandemic times - Archive ouverte HAL Access content directly
Conference Papers Year :

Recommender system in a non-stationary context: recommending job ads in pandemic times

(1, 2) , (3, 2) , (1) , (2, 3) , (2, 3) , (1, 4) , (5) , (3, 2)
1
2
3
4
5

Abstract

This paper focuses on the recommendation of job ads to job seekers, exploiting proprietary data from the French Public Employment Service (PES) and focusing more specifically on low or unskilled workers. Besides the usual challenges of data sparsity, the signal to noise ratio is high (few job seekers have diplomas), and scalability requirements are paramount. As a first contribution, a two-tiered approach is designed to handle these requirements; its empirical validation shows significant computational gains with no performance loss compared to boosted tree ensembles representative of the state of the art. A second contribution is a methodology aimed to assess the impact of the non-stationarity of the item and user distributions. Specifically, during the last three periods (before, during and after the Covid lock-downs), the numbers of job ads and job seekers dramatically vary in some industries. A normalized recall indicator is proposed to filter out the impact of variations of the number of job ads. This normalization suggests that the same score function adapts to the multi-faceted changes of the environment, resulting in different recommendations but with similar accuracy as before - at least for the job seekers finding a job.
Fichier principal
Vignette du fichier
FEAST2022_paper_3315.pdf (856.27 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03831247 , version 1 (27-10-2022)

Identifiers

  • HAL Id : hal-03831247 , version 1

Cite

Guillaume Bied, Solal Nathan, Elia Pérennes, Victor Alfonso Naya, Philippe Caillou, et al.. Recommender system in a non-stationary context: recommending job ads in pandemic times. FEAST workshop ECML-PKDD 2022, Sep 2022, Grenoble, France. ⟨hal-03831247⟩
0 View
0 Download

Share

Gmail Facebook Twitter LinkedIn More