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Results: 27
Number of items: 27
  • Open Access
    Khandel, P., Markov, I., Yates, A., & Varbanescu, A.-L. (2022). ParClick: A Scalable Algorithm for EM-based Click Models. In WWW'22: proceedings of the ACM Web Conference 2022 : April 25-29, 2022, VIrtual Event, Lyon, France (pp. 392-400). Association for Computing Machinery. https://doi.org/10.1145/3485447.3511967
  • Chen, Y., Wang, Y., Zhao, X., Yin, H., Markov, I., & De Rijke, M. (2020). Local Variational Feature-Based Similarity Models for Recommending Top-N New Items. ACM Transactions on Information Systems, 38(2), Article 12. https://doi.org/10.1145/3372154
  • Li, C., Markov, I., de Rijke, M., & Zoghi, M. (2020). MergeDTS: A Method for Effective Large-scale Online Ranker Evaluation. ACM Transactions on Information Systems, 38(4), Article 40. https://doi.org/10.1145/3411753
  • Jagerman, R., Markov, I., & de Rijke, M. (2020). Safe Exploration for Optimizing Contextual Bandits. ACM Transactions on Information Systems, 38(3), Article 24. https://doi.org/10.1145/3385670
  • Open Access
    Jagerman, R., Markov, I., & de Rijke, M. (2020). Safe Exploration for Optimizing Contextual Bandits. (pp. 23). ArXiv. https://doi.org/10.48550/arXiv.2002.00467
  • Open Access
    Jagerman, R. M. (2020). Efficient, safe and adaptive learning from user interactions. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Vardasbi, A., de Rijke, M., & Markov, I. (2020). Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank. 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. 2089-2092). Association for Computing Machinery. https://doi.org/10.1145/3397271.3401299
  • Jagerman, R., Markov, I., & de Rijke, M. (2019). When people change their mind: Off-policy evaluation in non-stationary recommendation environments. In WSDM'19: proceedings of the Twelfth ACM International Conference on Web Search and Data Mining : February 11-15, 2019 : Melbourne, Australia (pp. 447-455). Association for Computing Machinery. https://doi.org/10.1145/3289600.3290958
  • Akker, B. V. D., Markov, I., & Rijke, M. D. (2019). ViTOR: Learning to Rank Webpages Based on Visual Features [Data set]. DANS Data Station Physical and Technical Sciences. https://doi.org/10.17026/dans-xah-fkcq
  • Open Access
    Li, C., Kveton, B., Lattimore, T., Markov, I., de Rijke, M., Szepesvári, C., & Zoghi, M. (2019). BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, Tel Aviv, Israel, July 22-25, 2019 Article 47 AUAI Press. http://auai.org/uai2019/proceedings/papers/47.pdf
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