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Results: 1,025
Number of items: 1,025
  • Chen, Y., Wang, Y., Zhao, X., Zou, J., & de Rijke, M. (2020). Block-Aware Item Similarity Models for Top-N Recommendation. ACM Transactions on Information Systems, 38(4), Article 42. https://doi.org/10.1145/3411754
  • 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
  • Chen, W., Cai, F., Chen, H., & de Rijke, M. (2020). Hierarchical Neural Query Suggestion with an Attention Mechanism. Information Processing and Management, 57(6), Article 102040. https://doi.org/10.1016/j.ipm.2019.05.001
  • 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
    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
    Pan, Z., Cai, F., Chen, W., Chen, H., & de Rijke, M. (2020). Star Graph Neural Networks for Session-based Recommendation. In CIKM '20: proceedings of the 29th ACM International Conference on Information & Knowledge Management : October 19-23, 2020, Virtual Event, Ireland (pp. 1195–1204). The Association for Computing Machinery. https://doi.org/10.1145/3340531.3412014
  • 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
    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
  • Open Access
    Jagerman, R., & de Rijke, M. (2020). Accelerated Convergence 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. 469–478). Association for Computing Machinery. https://doi.org/10.1145/3397271.3401069
  • Open Access
    Jiang, S., Wolf, T., Monz, C., & de Rijke, M. (2020). TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2003.11963
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