• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 43 matches

• 2024
• 
  Yingshi Chen, Mohit Jain, Vaibhav Sawhney, Liyasi Wu:
  Enhancing Reliability in Recommendation Systems: Beyond point estimations to monitor population stability. HR@RecSys 2024
• 
  Jens-Joris Decorte, Jeroen Van Hautte, Chris Develder, Thomas Demeester:
  On the Biased Assessment of Expert Finding Systems. HR@RecSys 2024
• 
  Daniel Deniz, Federico Retyk, Laura García-Sardiña, Hermenegildo Fabregat, Luis Gascó, Rabih Zbib:
  Combined Unsupervised and Contrastive Learning for Multilingual Job Recommendation. HR@RecSys 2024
• 
  Warren Jouanneau, Marc Palyart, Emma Jouffroy:
  Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval. HR@RecSys 2024
• 
  Kento Nakada, Kazuki Kawamura, Ryosuke Furukawa:
  Parallel and Mini-Batch Stable Matching for Large-Scale Reciprocal Recommender Systems. HR@RecSys 2024
• 
  Federico Retyk, Luis Gascó, Casimiro Pio Carrino, Daniel Deniz, Rabih Zbib:
  MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations. HR@RecSys 2024
• 
  Roan Schellingerhout, Francesco Barile, Nava Tintarev:
  Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations. HR@RecSys 2024
• 
  Alejandro Seif, Sarah Toh, Hwee Kuan Lee:
  A Dynamic Jobs-Skills Knowledge Graph. HR@RecSys 2024
• 
  Laura Vásquez-Rodríguez, Bertrand Audrin, Samuel Michel, Samuele Galli, Julneth Rogenhofer, Jacopo Negro Cusa, Lonneke van der Plas:
  Hardware-effective Approaches for Skill Extraction in Job Offers and Resumes. HR@RecSys 2024
• 
  Liyasi Wu, Yi Wei Pang, Warren Cai:
  Pseudo-online Measurement of Retrieval Recall for Job Recommendations - A case study at Indeed (short paper). HR@RecSys 2024
• 
  Mesut Kaya, Toine Bogers, David Graus, Chris Johnson, Jens-Joris Decorte, Tijl De Bie:
  Proceedings of the 4th Workshop on Recommender Systems for Human Resources (RecSys-in-HR 2024) co-located with the 18th ACM Conference on Recommender Systems (RecSys 2024), Bari, Italy, 14th-18th October 2024. CEUR Workshop Proceedings 3788, CEUR-WS.org 2024 [contents]
• 2023
• 
  Spyros Avlonitis, Dor Lavi, Masoud Mansoury, David Graus:
  Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach. HR@RecSys 2023
• 
  Eric Behar, Julien Romero, Amel Bouzeghoub, Katarzyna Wegrzyn-Wolska:
  Tackling Cold Start for Job Recommendation with Heterogeneous Graphs. HR@RecSys 2023
• 
  Benjamin Clavié, Guillaume Soulié:
  Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers. HR@RecSys 2023
• 
  Jens-Joris Decorte, Jeroen Van Hautte, Johannes Deleu, Chris Develder, Thomas Demeester:
  Career Path Prediction using Resume Representation Learning and Skill-based Matching. HR@RecSys 2023
• 
  Deepak Kumar, Tessa Grosz, Elisabeth Greif, Navid Rekabsaz, Markus Schedl:
  Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning. HR@RecSys 2023
• 
  Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
  FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources. HR@RecSys 2023
• 
  Yuxin Luo, Feng Lu, Vaishali Pal, David Graus:
  Enhancing Resume Content Extraction in Question Answering Systems through T5 Model Variants. HR@RecSys 2023
• 
  Federico Retyk, Hermenegildo Fabregat, Juan Aizpuru, Mariana Taglio, Rabih Zbib:
  Résumé Parsing as Hierarchical Sequence Labeling: An Empirical Study. HR@RecSys 2023
• 
  Clara Rus, Maarten de Rijke, Andrew Yates:
  Counterfactual Representations for Intersectional Fair Ranking in Recruitment. HR@RecSys 2023
• 
  Jarno Vrolijk, David Graus:
  Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules. HR@RecSys 2023
• 
  Mesut Kaya, Toine Bogers, David Graus, Chris Johnson, Jens-Joris Decorte:
  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. CEUR Workshop Proceedings 3490, CEUR-WS.org 2023 [contents]
• 2022
• 
  Miriam Amin, Jan-Peter Bergmann, Yuri Campbell:
  Using vector representations for matching tasks to skills. HR@RecSys 2022
• 
  Adam Mehdi Arafan, David Graus, Fernando P. Santos, Emma Beauxis-Aussalet:
  End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates. HR@RecSys 2022
• 
  Jens-Joris Decorte, Jeroen Van Hautte, Johannes Deleu, Chris Develder, Thomas Demeester:
  Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction. HR@RecSys 2022
• 
  Wissem Inoubli, Armelle Brun:
  DGL4C: a Deep Semi-supervised Graph Representation Learning Model for Resume Classification. HR@RecSys 2022
• 
  Yichao Jin, Anirudh Alampally, Dheeraj Toshniwal, Zhiming Xu, Ankush Girdhar:
  Model Threshold Optimization for Segmented Job-Jobseeker Recommendation System. HR@RecSys 2022
• 
  Thom Lake:
  Flexible Job Classification with Zero-Shot Learning. HR@RecSys 2022
• 
  Benjamin Platten, Matthew Macfarlane, David Graus, Sepideh Mesbah:
  Automated Personnel Scheduling with Reinforcement Learning and Graph Neural Networks. HR@RecSys 2022
• 
  Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, Gido H. Schoenmacker:
  Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation. HR@RecSys 2022

skipping 13 more matches