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Vol-3490
urn:nbn:de:0074-3490-4
Copyright © 2023 for
the individual papers by the papers' authors.
Copyright © 2023 for the volume
as a collection by its editors.
This volume and its papers are published under the
Creative Commons License Attribution 4.0 International
(CC BY 4.0).
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RECSYS IN HR 2023
The 3rd Workshop on Recommender Systems for Human Resources (RecSys in HR 2023)
Proceedings of the 3rd Workshop on Recommender Systems for Human Resources (RecSys in HR 2023)
co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023)
Singapore, Singapore, 18th-22nd September 2023.
Edited by
*
Aalborg University Copenhagen,
Department of Communication and Psychology, Faculty of Humanities, Copenhagen, Denmark
**
IT University of Copenhagen,
Computer Science Department, Copenhagen, Denmark
***
Randstad,
Randstad Groep Nederland, Diemen, The Netherlands
****
Indeed,
Indeed, USA
*****
TechWolf,
TechWolf, Ghent, Belgium
Table of Contents
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Summary: There were 15 papers submitted for peer-review to this workshop. Out of these,
10 papers were accepted for this volume,
6 as regular papers and
4 as short papers.
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Career Path Prediction using Resume Representation Learning and Skill-based Matching
Jens-Joris Decorte,
Jeroen Van Hautte,
Johannes Deleu,
Chris Develder,
Thomas Demeester
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Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach
Spyros Avlonitis,
Dor Lavi,
Masoud Mansoury,
David Graus
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Counterfactual Representations for Intersectional Fair Ranking in Recruitment
Clara Rus,
Maarten de Rijke,
Andrew Yates
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Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules
Jarno Vrolijk,
David Graus
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Enhancing Resume Content Extraction in Question Answering Systems through T5 Model Variants
Yuxin Luo,
Feng Lu,
Vaishali Pal,
David Graus
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FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
Nan Li,
Bo Kang,
Jefrey Lijffijt,
Tijl De Bie
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Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning
Deepak Kumar,
Tessa Grosz,
Elisabeth Greif,
Navid Rekabsaz,
Markus Schedl
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Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers
Benjamin Clavié,
Guillaume Soulié
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Tackling Cold Start for Job Recommendation with Heterogeneous Graphs
Eric Behar,
Julien Romero,
Amel Bouzeghoub,
Katarzyna Wegrzyn-Wolska
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Résumé Parsing as Hierarchical Sequence Labeling: An Empirical Study
Federico Retyk,
Hermenegildo Fabregat,
Juan Aizpuru,
Mariana Taglio,
Rabih Zbib
2023-09-12: submitted by Mesut Kaya,
metadata incl. bibliographic data published under Creative Commons CC0
2023-09-23: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)
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