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Results: 35
Number of items: 35
  • Open Access
    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
  • Open Access
    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
  • 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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    Oosterhuis, H., & de Rijke, M. (2020). Unifying Online and Counterfactual Learning to Rank. ArXiv. https://arxiv.org/abs/2012.04426
  • Open Access
    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
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