David Graus

Publications

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2026

  •  [PDF] D. Boven, G. Behnke, and D. Graus, “Machina Pauliana: A neuro-symbolic approach to the actio pauliana in Dutch insolvency law,” in JURIX 2026, Toulouse, France, 2026.
     
  •  [PDF] A. Ranucci, E. Leijten, and D. Graus, “Towards More Granular Reasoning: Mapping the DSA Transparency Database Taxonomy to Platform Terms and Conditions Using Large Language Models,” in JURIX 2026, Toulouse, France, 2026.
     
  •  [PDF] M. Larooij and D. Graus, To Redact, or not to Redact? A Local LLM Approach to Deliberative Process Privilege Classification, 2026.
     
  •  [PDF] Ö. Ateş and D. Graus, Out of the Box: Zero-Shot Vision-Language Models for Redaction Detection and Page-Stream Segmentation, 2026.
     
  •  [PDF] [DOI] A. Parfenova, D. Graus, and J. Pfeffer, “From quotes to concepts: axial coding of political debates with ensemble lms,” in Advances in information retrieval: 48th european conference on information retrieval, ecir 2026, delft, the netherlands, march 29 – april 2, 2026, proceedings, part ii, Berlin, Heidelberg, 2026, p. 139–154.
     
  •  [PDF] [DOI] M. De Lange, W. Veys, F. Retyk, D. Deniz, W. Jouanneau, M. Zhang, A. L. Bielinski, E. Jouffroy, N. Clobes, N. Baranowska, D. Graus, M. Palyart, R. Zbib, D. Gkatzia, T. Demeester, T. De Bie, T. Bogers, J. Decorte, and J. Van Hautte, “Workrb: a community-driven evaluation framework for ai in the work domain,” in Proceedings of the 20th acm conference on recommender systems, New York, NY, USA, 2026, p. 933–938.
     
  •  [PDF] D. Graus, “From Legal Text to Executable Decision Models: Evaluating Structured Representations for Legal Decision Model Generation,” in Proceedings of the International Conference on Artificial Intelligence and Law (ICAIL), Singapore, 2026.
     

2025

  •  [PDF] [DOI] A. Fabris, N. Baranowska, M. J. Dennis, D. Graus, P. Hacker, J. Saldivar, F. Zuiderveen Borgesius, and A. J. Biega, “Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey,” ACM Trans. Intell. Syst. Technol., vol. 16, iss. 1, 2025.
     
  •  [PDF] [DOI] C. L. A. Clarke, P. Kantor, A. Roegiest, J. R. Trippas, Z. Ren, M. S. Bucarelli, X. Fu, Y. Fan, M. Granitzer, D. Graus, M. Heuss, J. Kamps, Y. Lei, A. Parry, D. Reijnaers, M. de Rijke, S. A. K. Singh, Y. Tang, S. Verberne, J. Wallat, Y. Wang, C. Xu, A. Yates, S. Zerhoudi, and J. Zhao, “Report on the 2nd Search Futures Workshop at ECIR 2025,” SIGIR Forum, vol. 59, iss. 1, p. 1–28, 2025.
     

2024

  •  [PDF] L. Rink, J. Meijdam, and D. Graus, “Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses,” in 1st Workshop on Natural Language Processing for Human Resources, 2024.
     
  •  [DOI] T. Bogers, D. Graus, M. Kaya, C. Johnson, J. Decorte, and T. De Bie, “Fourth Workshop on Recommender Systems for Human Resources (RecSys in HR 2024),” in Proceedings of the 18th ACM Conference on Recommender Systems, New York, NY, USA, 2024, p. 1222–1226.
     

2023

  •  [PDF] J. Vrolijk and D. Graus, “Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules,” in RecSys in HR’23: The 3rd Workshop on Recommender Systems for Human Resources, 2023.
     
  •  [PDF] Y. Luo, F. Lu, V. Pal, and D. Graus, “Enhancing Resume Content Extraction in Question Answering Systems through T5 Model Variants,” in RecSys in HR’23: The 3rd Workshop on Recommender Systems for Human Resources, 2023.
     
  •  [PDF] S. Avlonitis, D. Lavi, M. Mansoury, and D. Graus, “Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach,” in RecSys in HR’23: The 3rd Workshop on Recommender Systems for Human Resources, 2023.
     
  •  [DOI] T. Bogers, D. Graus, M. Kaya, C. Johnson, and J. Decorte, “Third Workshop on Recommender Systems for Human Resources (RecSys in HR 2023),” in Proceedings of the 17th ACM Conference on Recommender Systems, New York, NY, USA, 2023, p. 1244–1247.
     

2022

  •  [PDF] [DOI] D. Graus, T. Bogers, M. Kaya, F. Gutiérrez, and K. Verbert, “Report on the 1st Workshop on Recommender Systems for Human Resources (RecSys in HR 2021) at RecSys 2021,” SIGIR Forum, vol. 55, iss. 2, 2022.
     
  •  [PDF] S. van Els, D. Graus, and E. Beauxis-Aussalet, “Improving Fairness Assessments with Synthetic Data: a Practical Use Case with a Recommender System for Human Resources,” in CompJobs ’22: The First International Workshop on Computational Jobs Marketplace, 2022.
     
  •  [PDF] N. Vermeer, V. Provatorova, D. Graus, T. Rajapakse, and S. Mesbah, “Using RobBERT and eXtreme Multi-Label Classification to Extract Implicit and Explicit Skills From Dutch Job Descriptions,” in CompJobs ’22: The First International Workshop on Computational Jobs Marketplace, 2022.
     
  •  [PDF] A. M. Arafan, D. Graus, F. P. Santos, and E. Beauxis-Aussalet, “End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates,” in RecSys in HR’22: The 2nd Workshop on Recommender Systems for Human Resources, 2022.
     
  •  [PDF] B. Platten, M. Macfarlane, D. Graus, and S. Mesbah, “Automated Personnel Scheduling with Reinforcement Learning and Graph Neural Networks,” in RecSys in HR’22: The 2nd Workshop on Recommender Systems for Human Resources, 2022.
     
  •  [PDF] [DOI] A. Lőrincz, D. Graus, D. Lavi, and J. L. M. Pereira, “Transfer learning for multilingual vacancy text generation,” in Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 207–222.
     
  •  [DOI] T. Bogers, D. Graus, M. Kaya, F. Gutiérrez, S. Mesbah, and C. Johnson, “Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022),” in Proceedings of the 16th ACM Conference on Recommender Systems, New York, NY, USA, 2022, p. 671–674.
     
  •  Proceedings of the 2nd Workshop on Recommender Systems for Human Resources, 2022.
     

2021

  •  [PDF] M. de Groot, J. Schutte, and D. Graus, “Job Posting-Enriched Knowledge Graph for Skills-based Matching,” in Workshop on Recommender Systems for Human Resources (RecSys in HR), Amsterdam, Netherlands, 2021.
     
  •  [PDF] D. Lavi, V. Medentsiy, and D. Graus, “conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers,” in Workshop on Recommender Systems for Human Resources (RecSys in HR), Amsterdam, Netherlands, 2021.
     
  •  [PDF] [DOI] T. Bogers, D. Graus, M. Kaya, F. Gutiérrez, and K. Verbert, “RecSys in HR: Workshop on Recommender Systems for Human Resources,” in Proceedings of the Fifteenth ACM Conference on Recommender Systems, New York, NY, USA, 2021.
     
  •  Proceedings of the Workshop on Recommender Systems for Human Resources, 2021.
     

2020

  •  [PDF] [DOI] O. Berlage, K. Lux, and D. Graus, “Improving Automated Segmentation of Radio Shows with Audio Embeddings,” in ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 751-755.
     
  •  [PDF] [DOI] F. Lu, A. Dumitrache, and D. Graus, “Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation,” in Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, New York, NY, USA, 2020, p. 145–153.
     

2019

  •  [PDF] [DOI] E. Sullivan, D. Bountouridis, J. Harambam, S. Najafian, F. Loecherbach, M. Makhortykh, D. Kelen, D. Wilkinson, D. Graus, and N. Tintarev, “Reading news with a purpose: Explaining user profiles for self-actualization,” in Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, 2019, p. 241–245.
     

2018

  •  [PDF] [DOI] D. Graus, D. Odijk, and M. de Rijke, “The birth of collective memories: Analyzing emerging entities in text streams,” Journal of the Association for Information Science and Technology, vol. 69, iss. 6, pp. 773-786, 2018.
     
  •  A. Fourney, P. N. Bennett, R. White, E. Horvitz, R. Xin, and D. Graus, System for interpreting and managing imprecise temporal expressions, 2018.
     
  •  [PDF] D. Graus, M. Sappelli, and D. M. Chu, “Let me tell you who you are,” in The 2nd FATREC Workshop on Responsible Recommendation, 2018.
     
  •  [PDF] M. Sappelli, D. M. Chu, B. Cambel, J. Nortier, and D. Graus, “SMART Radio: Personalized News Radio,” in Proceedings of the 17th Dutch-Belgian Information Retrieval Workshop, 2018, p. 27.
     

2017

  •  [PDF] D. Graus, “Entities of Interest — Discovery in Digital Traces,” PhD Thesis, 2017.
     

2016

  •  [PDF] [DOI] D. Graus, P. N. Bennett, R. W. White, and E. Horvitz, “Analyzing and Predicting Task Reminders,” in Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, New York, NY, USA, 2016, p. 7–15.
     
  •  [PDF] [DOI] D. Graus, M. Tsagkias, W. Weerkamp, E. Meij, and M. de Rijke, “Dynamic Collective Entity Representations for Entity Ranking,” in Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, New York, NY, USA, 2016, p. 595–604.
     
  •  [PDF] M. de Rijke and D. Graus, “Wij zijn racisten, en daarom Google ook,” NRC Handelsblad, 2016.
     

2015

  •  [PDF] D. van Dijk, D. Graus, Z. Ren, H. Henseler, and M. de Rijke, “Who is Involved? Semantic Search for E-Discovery,” in ICAIL 2015 Workshop on Using Machine Learning and Other Advanced Techniques to Address Legal Problems in E-Discovery and Information Governance (DESI VI Workshop), 2015.
     
  •  C. Gârbacea, D. Odijk, D. Graus, I. Sijaranamual, and M. de Rijke, “Combining multiple signals for semanticizing tweets: University of Amsterdam at #Microposts2015,” in 5th Workshop on Making Sense of Microposts (#Microposts2015), 2015, p. 59–60.
     

2014

  •  [PDF] [DOI] D. Graus, M. Tsagkias, L. Buitinck, and M. de Rijke, “Generating Pseudo-ground Truth for Predicting New Concepts in Social Streams,” in Advances in Information Retrieval, Cham, 2014, p. 286–298.
     
  •  [PDF] [DOI] D. Graus, D. van Dijk, M. Tsagkias, W. Weerkamp, and M. de Rijke, “Recipient Recommendation in Enterprises Using Communication Graphs and Email Content,” in Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, New York, NY, USA, 2014, p. 1079–1082.
     
  •  [PDF] [DOI] D. Graus, D. Odijk, M. Tsagkias, W. Weerkamp, and M. de Rijke, “Semanticizing Search Engine Queries: The University of Amsterdam at the ERD 2014 Challenge,” in Proceedings of the First International Workshop on Entity Recognition & Disambiguation, New York, NY, USA, 2014, p. 69–74.
     

2013

  •  [PDF] D. Odijk, E. Meij, D. Graus, and T. Kenter, “Multilingual semantic linking for video streams: Making “ideas worth sharing” more accessible,” in Proceedings of the 2nd International Workshop on Web of Linked Entities (WoLE 2013), 2013.
     
  •  [PDF] D. Graus, Z. Ren, M. De Rijke, D. Van Dijk, H. Henseler, and N. Van Der Knaap, “Semantic search in e-discovery: An interdisciplinary approach,” in ICAIL 2013 Workshop on Standards for Using Predictive Coding, Machine Learning, and Other Advanced Search and Review Methods in E-Discovery (DESI V Workshop), 2013.
     
  •  [PDF] Z. Ren, D. Van Dijk, D. Graus, N. Van Der Knaap, H. Henseler, and M. De Rijke, “Semantic linking and contextualization for social forensic text analysis,” in 2013 European Intelligence and Security Informatics Conference, 2013, p. 96–99.
     
  •  [PDF] T. Kenter, D. Graus, E. Meij, M. De Rijke, and others, “Time-aware chi-squared for document filtering over time,” in Proceedings of the SIGIR 2013 Workshop on Time-aware Information Access, 2013.
     
  •  [PDF] D. Graus, M. Peetz, D. Odijk, O. de Rooij, and M. de Rijke, “yourHistory–Semantic linking for a personalized timeline of historic events,” in Proceedings of the LinkedUp Veni Competition on Linked and Open Data, 2013.
     

2012

  •  [PDF] D. Graus, T. Kenter, M. Bron, E. Meij, and M. De Rijke, “Context-based entity linking-University of Amsterdam at TAC 2012,” in Proceedings of the Fifth Text Analysis Conference (TAC’12). NIST, 2012, p. 13.