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Results: 1,033
Number of items: 1,033
  • Open Access
    Sepliarskaia, A., Genc, S., & de Rijke, M. (2021). A Deep Reinforcement Learning-Based Approach to Query-Free Interactive Target Item Retrieval. In Proceedings of the 2021 SIGIR Workshop on eCommerce (SIGIR eCom’20): July 15, 2021, Virtual Event, Montreal, Canada Article workshop paper 1 ACM. https://sigir-ecom.github.io/ecom21Papers/paper2.pdf
  • Open Access
    Kallumadi, S., King, T. H., Malmasi, S., & de Rijke, M. (2021). ECOM’21: The SIGIR 2021 Workshop on eCommerce. In SIGIR '21: proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 11-15, 2021, virtual event, Canada (pp. 2685-2688). Association for Computing Machinery. https://doi.org/10.1145/3404835.3462820
  • Open Access
    Sarvi, F., Voskarides, N., Mooiman, L., Schelter, S., & de Rijke, M. (2020). A Comparison of Supervised Learning to Match Methods for Product Search. In The 2020 SIGIR Workshop On eCommerce: July 30 : accepted papers Article 30 SIGIR eCom'20. https://sigir-ecom.github.io/ecom20Papers/paper30.pdf
  • 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. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2012.04426
  • Li, C., Feng, H., & de Rijke, M. (2020). Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity. In RECSYS 2020: 14th ACM Conference on Recommender Systems : Virtual Event, Brazil, September 22-26, 2020 (pp. 33–42). The Association for Computing Machinery. https://doi.org/10.1145/3383313.3412245
  • Open Access
    Li, X., de Rijke, M., Liu, Y., Mao, J., Ma, W., Zhang, M., & Ma, S. (2020). Learning Better Representations for Neural Information Retrieval with Graph Information. In CIKM '20: proceedings of the 29th ACM International Conference on Information & Knowledge Management : October 19-23, 2020, Virtual Event, Ireland (pp. 795-804). The Association for Computing Machinery. https://doi.org/10.1145/3340531.3411957
  • Open Access
    Li, Z., Kiseleva, J., & de Rijke, M. (2020). Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems. In T. Cohn, Y. He, & Y. Liu (Eds.), Findings of the Association for Computational Linguistics. Findings of ACL: EMNLP 2020: 16-20 November, 2020 (pp. 3537–3546). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.findings-emnlp.316
  • Open Access
    Ariannezhad, M., Schelter, S., & de Rijke, M. (2020). Demand Forecasting in the Presence of Privileged Information. In V. Lemaire, S. Malinowski, A. Bagnall, T. Guyet, R. Tavenard, & G. Ifrim (Eds.), Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020 : revised selected papers (pp. 46-62). (Lecture Notes in Computer Science; Vol. 12588), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-65742-0_4
  • Open Access
    Chen, W., Ren, P., Cai, F., Sun, F., & de Rijke, M. (2020). Improving End-to-End Sequential Recommendations with Intent-aware Diversification. In CIKM '20: proceedings of the 29th ACM International Conference on Information & Knowledge Management : October 19-23, 2020, Virtual Event, Ireland (pp. 175–184). The Association for Computing Machinery. https://doi.org/10.1145/3340531.3411897
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