RecSys-in-HR 2022
Recommender Systems for Human Resources 2022

Table of Contents

• Summary: There were 12 papers submitted for peer-review to this workshop. Out of these, 11 papers were accepted for this volume, 11 as regular papers.

• Automated Personnel Scheduling with Reinforcement Learning and Graph Neural Networks
  Benjamin Platten, Matthew Macfarlane, David Graus, Sepideh Mesbah
• Beyond human-in-the-loop: scaling occupation taxonomy at Indeed
  Suyi Tu, Olivia Cannon
• Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation
  Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, Gido H. Schoenmacker
• Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction
  Jens-Joris Decorte, Jeroen Van Hautte, Johannes Deleu, Chris Develder, Thomas Demeester
• DGL4C: a Deep Semi-supervised Graph Representation Learning Model for Resume Classification
  Wissem Inoubli, Armelle Brun
• End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates
  Adam Mehdi Arafan, David Graus, Fernando P. Santos, Emma Beauxis-Aussalet
• Explainable Career Path Predictions using Neural Models
  Roan Schellingerhout, Volodymyr Medentsiy, Maarten Marx
• Flexible Job Classification with Zero-Shot Learning
  Thom Lake
• Model Threshold Optimization for Segmented Job-Jobseeker Recommendation System
  Yichao Jin, Anirudh Alampally, Dheeraj Toshniwal, Zhiming Xu, Ankush Girdhar
• Skill Extraction from Job Postings using Weak Supervision
  Mike Zhang, Kristian Nørgaard Jensen, Rob van der Goot, Barbara Plank
• Using vector representations for matching tasks to skills
  Miriam Amin, Jan-Peter Bergmann, Yuri Campbell