Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Graus, David"'
Understanding preferences, opinions, and sentiment of the workforce is paramount for effective employee lifecycle management. Open-ended survey responses serve as a valuable source of information. This paper proposes a machine learning approach for a
Externí odkaz:
http://arxiv.org/abs/2402.04812
Autor:
Fabris, Alessandro, Baranowska, Nina, Dennis, Matthew J., Graus, David, Hacker, Philipp, Saldivar, Jorge, Borgesius, Frederik Zuiderveen, Biega, Asia J.
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides
Externí odkaz:
http://arxiv.org/abs/2309.13933
This study explores the potential of reinforcement learning algorithms to enhance career planning processes. Leveraging data from Randstad The Netherlands, the study simulates the Dutch job market and develops strategies to optimize employees' long-t
Externí odkaz:
http://arxiv.org/abs/2309.05391
Autor:
Vrolijk, Jarno, Graus, David
The increased digitization of the labour market has given researchers, educators, and companies the means to analyze and better understand the labour market. However, labour market resources, although available in high volumes, tend to be unstructure
Externí odkaz:
http://arxiv.org/abs/2308.16770
In this paper we focus on constructing useful embeddings of textual information in vacancies and resumes, which we aim to incorporate as features into job to job seeker matching models alongside other features. We explain our task where noisy data fr
Externí odkaz:
http://arxiv.org/abs/2109.06501
The labor market is constantly evolving. Occupations are changing, being added, or disappearing to fit the needs of today's market. In recent years the pace of this change has accelerated, due to factors such as globalization, digitization, and the s
Externí odkaz:
http://arxiv.org/abs/2109.02554
Autor:
Graus, David
In the era of big data, we continuously - and at times unknowingly - leave behind digital traces, by browsing, sharing, posting, liking, searching, watching, and listening to online content. When aggregated, these digital traces can provide powerful
Externí odkaz:
http://arxiv.org/abs/2102.10962
With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In this paper we study an automated
Externí odkaz:
http://arxiv.org/abs/2004.09980
Audio features have been proven useful for increasing the performance of automated topic segmentation systems. This study explores the novel task of using audio embeddings for automated, topically coherent segmentation of radio shows. We created thre
Externí odkaz:
http://arxiv.org/abs/2002.05194
We study how collective memories are formed online. We do so by tracking entities that emerge in public discourse, that is, in online text streams such as social media and news streams, before they are incorporated into Wikipedia, which, we argue, ca
Externí odkaz:
http://arxiv.org/abs/1701.04039