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Results: 1,032
Number of items: 1,032
  • Open Access
    Jiang, S., Ren, P., Monz, C., & de Rijke, M. (2019). Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss. In The Web Conference 2019: proceedings of the World Wide Web Conference WWW 2019 : May 13-17, 2019, San Francisco, CA, USA (pp. 2879-2885). Association for Computing Machinery. https://doi.org/10.1145/3308558.3313415
  • Open Access
    Li, C., & de Rijke, M. (2019). Cascading Non-stationary Bandits: Online Learning to Rank in the Non-stationary Cascade Model. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.1905.12370
  • Open Access
    van den Bosch, A., van Dijck, J., Helberger, N., Heylen, D., Hindriks, K., Hoos, H., Lagendijk, I., de Rijke, M., Niessen, W., Verheij, B., Vossen, P., & van Wynsberghe, A. (2019). Artificial Intelligence Research Agenda for the Netherlands. NWO. https://www.nwo.nl/en/news/first-national-research-agenda-artificial-intelligence
  • Open Access
    Zhang, Y., Ren, P., & de Rijke, M. (2019). Improving Background Based Conversation with Context-aware Knowledge Pre-selection. Paper presented at 4th International Workshop on Search-Oriented Conversational AI (SCAI), Macao, China. https://arxiv.org/abs/1906.06685
  • Open Access
    Li, C., Feng, H., & de Rijke, M. (2019). A Contextual-Bandit Approach to Online Learning to Rank for Relevance and Diversity. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.1912.00508
  • Open Access
    Dehghani, M., Azarbonyad, H., Kamps, J., & de Rijke, M. (2019). Learning to Transform, Combine, and Reason in Open-Domain Question Answering. In K. Beuls, B. Bogaerts, G. Bontempi, P. Geurts, N. Harley, B. Lebichot, T. Lenaerts, G. Louppe, & P. Van Eecke (Eds.), Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019): Brussels, Belgium, November 6-8, 2019 Article 16 (CEUR Workshop Proceedings; Vol. 2491). CEUR-WS. http://ceur-ws.org/Vol-2491/abstract16.pdf
  • Open Access
    Azarbonyad, H., Dehghani, M., Kenter, T., Marx, M., Kamps, J., & de Rijke, M. (2019). HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents. IEEE Transactions on Knowledge and Data Engineering, 31(11), 2124-2137 . https://doi.org/10.1109/TKDE.2018.2874246
  • Open Access
    van den Akker, B., Markov, I., & de Rijke, M. (2019). ViTOR: Learning to rank webpages based on visual features. In The Web Conference 2019: proceedings of the World Wide Web Conference WWW 2019 : May 13-17, 2019, San Francisco, CA, USA (pp. 3279-3285). Association for Computing Machinery. https://doi.org/10.1145/3308558.3313419
  • Open Access
    Li, C., & de Rijke, M. (2019). Cascading non-stationary bandits: Online learning to rank in the non-stationary cascade model. In S. Kraus (Ed.), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence: IJCAI-19 : Macao, 10-16 August 2019 (pp. 2859-2865). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/396
  • Open Access
    Zheng, J., Cai, F., Chen, H., & de Rijke, M. (2019). Pre-train, Interact, Fine-tune: A Novel Interaction Representation for Text Classification. ArXiv. https://arxiv.org/abs/1909.11824
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