| 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).

# 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

### Mesut Kaya *
Toine Bogers **
David Graus ***
Chris Johnson ****
Jens-Joris Decorte *****

* 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

- 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.

- Career Path Prediction using Resume Representation Learning and Skill-based Matching
  Jens-Joris Decorte, Jeroen Van Hautte, Johannes Deleu, Chris Develder, Thomas Demeester
- Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach
  Spyros Avlonitis, Dor Lavi, Masoud Mansoury, David Graus
- Counterfactual Representations for Intersectional Fair Ranking in Recruitment
  Clara Rus, Maarten de Rijke, Andrew Yates
- Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules
  Jarno Vrolijk, David Graus
- Enhancing Resume Content Extraction in Question Answering Systems through T5 Model Variants
  Yuxin Luo, Feng Lu, Vaishali Pal, David Graus
- FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
  Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie
- 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
- Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers
  Benjamin Clavié, Guillaume Soulié
- Tackling Cold Start for Job Recommendation with Heterogeneous Graphs
  Eric Behar, Julien Romero, Amel Bouzeghoub, Katarzyna Wegrzyn-Wolska
- 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) |valid HTML5|