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Results: 1,033
Number of items: 1,033
  • Open Access
    Gao, C., Lei, W., He, X., de Rijke, M., & Chua, T.-S. (2021). Advances and Challenges in Conversational Recommender Systems: A Survey. (v7 ed.) ArXiv. https://doi.org/10.48550/arXiv.2101.09459
  • Open Access
    Zhang, Y., Ren, P., & de Rijke, M. (2021). A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: ACL-IJCNLP 2021 : proceedings of the conference : August 1-6, 2021 (Vol. 1, pp. 5612–5623). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.436
  • 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., & 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
    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
  • Open Access
    ter Hoeve, M., Sim, R., Nouri, E., Fourney, A., de Rijke, M., & White, R. W. (2020). Conversations with Documents: An Exploration of Document-Centered Assistance. In CHIIR '20: proceedings of the 2020 Conference on Human Information Interaction and Retrieval : March 14-18, 2020, Vancouver, BC, Canada (pp. 43-52). The Association for Computing Machinery. https://doi.org/10.1145/3343413.3377971
  • Open Access
    Wu, C., Kanoulas, E., & de Rijke, M. (2020). Learning entity-centric document representations using an entity facet topic model. Information Processing and Management, 57(3), Article 102216. https://doi.org/10.1016/j.ipm.2020.102216
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