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Results: 1,025
Number of items: 1,025
  • Open Access
    Bénédict, G. (2024). A machine learning personalization flow. [Thesis, fully internal, Universiteit van Amsterdam].
  • Ma, M., Ren, P., Chen, Z., Ren, Z., Liang, H., Ma, J., & De Rijke, M. (2023). Improving Transformer-based Sequential Recommenders through Preference Editing. ACM Transactions on Information Systems, 41(3), Article 71. https://doi.org/10.1145/3564282
  • Deffayet, R., Renders, J.-M., & de Rijke, M. (2023). Evaluating the Robustness of Click Models to Policy Distributional Shift. ACM Transactions on Information Systems, 41(4), Article 84. https://doi.org/10.1145/3569086
  • Rajapakse, T. C., & de Rijke, M. (2023). Improving the Generalizability of the Dense Passage Retriever. In J. Kamps, L. Goeuriot, F. Crestani, M. Maistro, H. Joho, B. Davis, C. Gurrin, U. Kruschwitz, & A. Caputo (Eds.), Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023 : proceedings (Vol. II, pp. 94-109). (Lecture Notes in Computer Science; Vol. 13981). Springer. https://doi.org/10.1007/978-3-031-28238-6_7
  • Deffayet, R., Hager, P., Renders, J.-M., & de Rijke, M. (2023). An Offline Metric for the Debiasedness of Click Models. 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. 558–568). Association for Computing Machinery. https://doi.org/10.1145/3539618.3591639
  • Hager, P., de Rijke, M., & Zoeter, O. (2023). Contrasting Neural Click Models and Pointwise IPS Rankers. In J. Kamps, L. Goeuriot, F. Crestani, M. Maistro, H. Joho, B. Davis, C. Gurrin, U. Kruschwitz, & A. Caputo (Eds.), Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023 : proceedings (Vol. I, pp. 409-425). (Lecture Notes in Computer Science; Vol. 13980). Springer. https://doi.org/10.1007/978-3-031-28244-7_26
  • Ling, Y., Cai, F., Liu, J., Chen, H., & de Rijke, M. (2023). Keep and Select: Improving hierarchical context modeling for multi-turn response generation. IEEE Transactions on Neural Networks and Learning Systems, 34(7), 3636-3649. https://doi.org/10.1109/TNNLS.2021.3112700
  • Open Access
    Jullien, S., Ariannezhad, M., Groth, P., & Rijke, M. D. (2023). A Simulation Environment and Reinforcement Learning Method for Waste Reduction. Transactions on Machine Learning Research, 2023, Article 769. https://openreview.net/forum?id=KSvr8A62MD
  • Open Access
    Meng, C., Arabzadeh, N., Aliannejadi, M., & de Rijke, M. (2023). Query Performance Prediction: From Ad-hoc to Conversational Search. 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. 2583-2593). Association for Computing Machinery. https://doi.org/10.1145/3539618.3591919
  • Open Access
    Pal, V., Yates, A., Kanoulas, E., & de Rijke, M. (2023). MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), The 61st Conference of the Association for Computational Linguistics: Proceedings of the Conference : ACL 2023 : July 9-14, 2023 (Vol. 1, pp. 6322–6334). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.348
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