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Dr. H.R. (Harrie) Oosterhuis MSc

Faculty of Science
Informatics Institute
Visiting address
  • Science Park 900
Postal address
  • Postbus 94323
    1090 GH Amsterdam
Contact details
  • Publications

    2025

    • de Leon-Martinez, S., Kang, J., Moro, R., de Rijke, M., Kveton, B., Oosterhuis, H., & Bielikova, M. (2025). RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces. In SIGIR '25: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 13-18, 2025, Padua, Italy (pp. 3702-3711). Association for Computing Machinery. https://doi.org/10.1145/3726302.3730301 [details]

    2024

    • Gupta, S., Hager, P., Huang, J., Vardasbi, A., & Oosterhuis, H. (2024). Unbiased Learning to Rank: On Recent Advances and Practical Applications. In WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining : March 4-8, 2024, Merida, Mexico (pp. 1118–1121). Association for Computing Machinery. https://doi.org/10.1145/3616855.3636451 [details]
    • Gupta, S., Oosterhuis, H., & de Rijke, M. (2024). Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank. In CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management : October, 21-25. 2024, Boise, ID, USA (pp. 737-747). Association for Computing Machinery. https://doi.org/10.1145/3627673.3679531 [details]
    • Huang, J., Oosterhuis, H., Mansoury, M., van Hoof, H., & de Rijke, M. (2024). Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. In SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 14-18, 2024, Washington, DC, USA (pp. 416-426). Association for Computing Machinery. https://doi.org/10.1145/3626772.3657749 [details]

    2023

    • Gupta, S., Hager, P., Huang, J., Vardasbi, A., & Oosterhuis, H. (2023). Recent Advances in the Foundations and Applications of Unbiased Learning to Rank. In SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 23-27, 2023, Taipei, Taiwan (pp. 3440–3443). Association for Computing Machinery. https://doi.org/10.1145/3539618.3594247 [details]
    • Gupta, S., Oosterhuis, H., & de Rijke, M. (2023). A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback. In ICTIR '23: Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval : July 23, 2023, Taipei, Taiwan (pp. 87–93). Association for Computing Machinery. https://doi.org/10.1145/3578337.3605114 [details]
    • Gupta, S., Oosterhuis, H., & de Rijke, M. (2023). Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization. In SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 23-27, 2023, Taipei, Taiwan (pp. 249–258). Association for Computing Machinery. https://doi.org/10.1145/3539618.3591760 [details]

    2022

    • Huang, J., Oosterhuis, H., & de Rijke, M. (2022). It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic. In WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining : February 21-25, 2022 : virtual event, Tempe, AZ, USA (pp. 381–389). Association for Computing Machinery. https://doi.org/10.1145/3488560.3498375 [details]
    • Huang, J., Oosterhuis, H., Cetinkaya, B., Rood, T., & de Rijke, M. (2022). State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. In SIGIR '22: proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 11-15, 2022, Madrid, Spain (pp. 2738-2748). The Association for Computing Machinery. https://doi.org/10.1145/3477495.3531716 [details]

    2021

    • Oosterhuis, H., & de Rijke, M. (2021). Robust Generalization and Safe Query-specialization in Counterfactual Learning to Rank. In The Web Conference 2021: proceedings of the World Wide Web Conference WWW 2021 : April 19-23, 2021, Ljubljana, Slovenia (pp. 158-170). Association for Computing Machinery. https://doi.org/10.1145/3442381.3450018 [details]
    • Oosterhuis, H., & de Rijke, M. (2021). Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions. In WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining : March 8-12, 2021, virtual event, Israel (pp. 463-471). Association for Computing Machinery. https://doi.org/10.1145/3437963.3441794 [details]

    2020

    • Huang, J., Oosterhuis, H., de Rijke, M., & van Hoof, H. (2020). Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. In RECSYS 2020: 14th ACM Conference on Recommender Systems : Virtual Event, Brazil, September 22-26, 2020 (pp. 190–199). The Association for Computing Machinery. https://doi.org/10.1145/3383313.3412252 [details]
    • Oosterhuis, H., & de Rijke, M. (2020). Policy-Aware Unbiased Learning to Rank for Top-k Rankings. In SIGIR '20: proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 25-30, 2020, virtual event, China (pp. 489–498). Association for Computing Machinery. https://doi.org/10.1145/3397271.3401102 [details]
    • Oosterhuis, H., & de Rijke, M. (2020). Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. In ICTIR'20: proceedings of the 2020 ACM SIGIR International Conference on Theory of Information Retrieval : September 14-17, 2020, Virtual Event, Norway (pp. 137–144). The Association for Computing Machinery. https://doi.org/10.1145/3409256.3409820 [details]
    • Oosterhuis, H., Jagerman, R., & de Rijke, M. (2020). Unbiased Learning to Rank: Counterfactual and Online Approaches. In The Web Conference 2020: companion of the World Wide Web Conference WWW 2020 : Taipei 2020 : April 20-24, 2020, Taipei, Taiwan (pp. 299-300). International World Wide Web Conference Committee. https://doi.org/10.1145/3366424.3383107 [details]
    • Vardasbi, A., Oosterhuis, H., & de Rijke, M. (2020). When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. In CIKM '20: proceedings of the 29th ACM International Conference on Information & Knowledge Management : October 19-23, 2020, Virtual Event, Ireland (pp. 1475–1484). The Association for Computing Machinery. https://doi.org/10.1145/3340531.3412031 [details]

    2019

    • Jagerman, R., Oosterhuis, H., & de Rijke, M. (2019). To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. In SIGIR '19: proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 21-25, 2019, Paris, France (pp. 15-24). The Association for Computing Machinery. https://doi.org/10.1145/3331184.3331269 [details]
    • Lucchesee, C., Nardini, F. M., Pasumarthi, R. K., Bruch, S., Bendersky, M., Wang, X., Oosterhuis, H., Jagerman, R., & de Rijke, M. (2019). Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning. In SIGIR '19: proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 21-25, 2019, Paris, France (pp. 1419-1420). The Association for Computing Machinery. https://doi.org/10.1145/3331184.3334824 [details]
    • Oosterhuis, H., & de Rijke, M. (2019). Optimizing Ranking Models in an Online Setting. In L. Azzopardi, B. Stein, N. Fuhr, P. Mayr, C. Hauff, & D. Hiemstra (Eds.), Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14-18, 2019 : proceedings (Vol. 1, pp. 382-396). (Lecture Notes in Computer Science; Vol. 11437). Springer. https://doi.org/10.1007/978-3-030-15712-8_25 [details]

    2018

    • Oosterhuis, H., & de Rijke, M. (2018). Differentiable Unbiased Online Learning to Rank. In CIKM'18: proceedings of the 2018 ACM International Conference on Information and Knowledge Management : October 22-26, 2018, Torino, Italy (pp. 1293-1302). The Association for Computing Machinery. https://doi.org/10.1145/3269206.3271686 [details]
    • Oosterhuis, H., & de Rijke, M. (2018). Ranking for Relevance and Display Preferences in Complex Presentation Layouts. In SIGIR #41 proceedings: Ann Arbor, Michigan, USA, 08-12, July 2018 (pp. 845-854). Association for Computing Machinery. https://doi.org/10.1145/3209978.3209992 [details]
    • Oosterhuis, H., Culpepper, J. S., & de Rijke, M. (2018). The Potential of Learned Index Structures for Index Compression. In B. Koopman, A. Trotman, & P. Thomas (Eds.), ADCS 2018: proceedings of the 23rd Australasian Document Computing Symposium : Dunedin, New Zealand, December 11-12, 2018 Article 7 ACM. https://doi.org/10.1145/3291992.3291993 [details]

    2017

    • Jagerman, R., Oosterhuis, H., & de Rijke, M. (2017). Query-level Ranker Specialization. In N. Ferro, C. Lucchese, M. Maistro, & R. Perego (Eds.), Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers: co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017) : Amsterdam, The Netherlands, October 1, 2017 (CEUR Workshop Proceedings; Vol. 2007). CEUR-WS. http://ceur-ws.org/Vol-2007/LEARNER2017_full_2.pdf [details]
    • Oosterhuis, H., & de Rijke, M. (2017). Balancing Speed and Quality in Online Learning to Rank for Information Retrieval. In CIKM'17 : proceedings of the 2017 ACM on Conference on Information and Knowledge Management: November 6-10, 2017, Singapore, Singapore (pp. 277-286). Association for Computing Machinery. https://doi.org/10.1145/3132847.3132896 [details]
    • Oosterhuis, H., & de Rijke, M. (2017). Sensitive and Scalable Online Evaluation with Theoretical Guarantees. In CIKM'17 : proceedings of the 2017 ACM on Conference on Information and Knowledge Management: November 6-10, 2017, Singapore, Singapore (pp. 77-86). Association for Computing Machinery. https://doi.org/10.1145/3132847.3132895 [details]

    2016

    • Oosterhuis, H., Schuth, A., & de Rijke, M. (2016). Probabilistic Multileave Gradient Descent. In N. Ferro, F. Crestani, M.-F. Moens, J. Mothe, F. Silvestri, G. M. Di Nunzio, C. Hauff, & G. Silvello (Eds.), Advances in Information Retrieval: 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20-23, 2016 : proceedings (pp. 661-668). (Lecture Notes in Computer Science; Vol. 9626). Springer. https://doi.org/10.1007/978-3-319-30671-1_50 [details]
    • Schuth, A., Oosterhuis, H., Whiteson, S., & de Rijke, M. (2016). Multileave Gradient Descent for Fast Online Learning to Rank. In WSDM'16 : proceedings of the Ninth ACM International Conference on Web Search and Data Mining : February 22-25, 2016, San Francisco, CA, USA (pp. 457-466). Association for Computing Machinery. https://doi.org/10.1145/2835776.2835804 [details]

    2015

    • Schuth, A., Bruintjes, R.-J., Büttner, F., van Doorn, J., Groenland, C., Oosterhuis, H., Tran, C.-N., Veeling, B., van der Velde, J., Wechsler, R., Woudenberg, D., & de Rijke, M. (2015). Probabilistic Multileave for Online Retrieval Evaluation. In SIGIR 2015: proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval: August 9-13, 2015, Santiago, Chile (pp. 955-958). Association for Computing Machinery. https://doi.org/10.1145/2766462.2767838 [details]

    2023

    • Gupta, S., Hager, P., & Oosterhuis, H. (2023). Recent Advancements in Unbiased Learning to Rank. In D. Ganguly, S. Majumdar, B. Mitra, P. Gupta, S. Gangopadhyay, & P. Majemder (Eds.), FIRE 2023: Proceedings of the 15th annual meeting of the Forum for Information Retrieval Evaluation : Goa University, Panjim, India, December 15-18, 2023 (pp. 145-148). (ACM International Conference Proceedings Series). The Association for Computing Machinery. https://doi.org/10.1145/3632754.3632942 [details]

    2006

    • Pach, H. (2006). 'Die Amsterdamer Dinstagishe un Fraitagishe Kurantn (1686-1687). Wie jüdisch war die erste Jiddische Zeitung?'. In S. Marten-Finnis, & M. Winkler (Eds.), Die jüdische Presse im europäischen Kontext 1686-1990 (pp. 17-25). Bremen.

    2022

    • Lucic, A., Oosterhuis, H., Haned, H., & de Rijke, M. (2022). FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles. Poster session presented at 36th AAAI Conference on Artificial Intelligence (AAAI-2022). https://doi.org/10.48550/arXiv.1911.12199

    2015

    2025

    • Gupta, S. (2025). Safe, efficient and robust reinforcement learning for ranking and diffusion models. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2024

    • Huang, J. (2024). Learning recommender systems from biased user interactions. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2020

    • Oosterhuis, H. R. (2020). Learning from user interactions with rankings: A unification of the field. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2014

    2020

    2019

    • Lucic, A., Oosterhuis, H., Haned, H., & de Rijke, M. (2019). Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles. (v1 ed.) ArXiv. [details]

    2026

    • de Leon-Martinez, S., Kang, J., Móro, R., de Rijke, M., Kveton, B., Oosterhuis, H. & Bielikova, M. (27-3-2026). RecGaze Dataset - Non-Public Version. Zenodo. https://doi.org/10.5281/zenodo.19256039

    2025

    • de Leon-Martinez, S., Kang, J., Móro, R., de Rijke, M., Kveton, B., Oosterhuis, H. & Bielikova, M. (29-4-2025). RecGaze Dataset - Public Version. Zenodo. https://doi.org/10.5281/zenodo.15270518
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  • Ancillary activities
    • Amazon Music
      Amazon Research Scholar working on Personalization for Music Recommendation.
    • No ancillary activities
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