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Results: 1,033
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  • Olteanu, A., Garcia-Gathright, J., de Rijke, M., & Ekstrand, M. D. (2019). Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval (FACTS-IR). In SIGIR '19: proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 21-25, 2019, Paris, France (pp. 1423-1425). The Association for Computing Machinery. https://doi.org/10.1145/3331184.3331644
  • Shao, T., Cai, F., Chen, H., & de Rijke, M. (2019). Length-adaptive Neural Network for Answer Selection. In SIGIR '19: proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 21-25, 2019, Paris, France (pp. 869-872). The Association for Computing Machinery. https://doi.org/10.1145/3331184.3331277
  • Vakulenko, S., Revoredo, K., Di Ciccio, C., & de Rijke, M. (2019). QRFA: A Data-driven Model of Information-Seeking Dialogues. In L. Azzopardi, B. Stein, N. Fuhr, P. Mayr, C. Hauff, & D. Hiemstra (Eds.), Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14-18, 2019 : proceedings (Vol. 1, pp. 541-557). (Lecture Notes in Computer Science; Vol. 11437). Springer. https://doi.org/10.1007/978-3-030-15712-8_35
  • Xie, X., Mao, J., Liu, Y., de Rijke, M., Ai, Q., Huang, Y., Zhang, M., & Ma, S. (2019). Improving Web Image Search with Contextual Information. In CIKM'19: Proceedings of the 28th ACM International Conference on Information & Knowledge Management : November 3-7, 2019, Beijing, China (pp. 1683-1692). Association for Computing Machinery. https://doi.org/10.1145/3357384.3358011
  • 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
  • Jagerman, R., Oosterhuis, H., & de Rijke, M. (2019). To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. In SIGIR '19: proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 21-25, 2019, Paris, France (pp. 15-24). The Association for Computing Machinery. https://doi.org/10.1145/3331184.3331269
  • Wang, S., Ren, P., Chen, Z., Ren, Z., Ma, J., & de Rijke, M. (2019). Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning. In CIKM'19: Proceedings of the 28th ACM International Conference on Information & Knowledge Management : November 3-7, 2019, Beijing, China (pp. 1623-1632). Association for Computing Machinery. https://doi.org/10.1145/3357384.3357965
  • Ren, P., Chen, Z., Li, J., Ren, Z., Ma, J., & de Rijke, M. (2019). RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation. In Thirty-Third AAAI Conference on Artificial Intelligence, Thirty-First Conference on Innovative Applications of Artificial Intelligence, The Ninth Symposium on Educational Advances in Artificial Intelligence: AAAI-19, IAAI-19, EAAI-20 : January 27-February 1, 2019, Hilton Hawaiian Village, Honolulu, Hawaii, USA (pp. 4806-4813). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 33). AAAI Press. https://doi.org/10.1609/aaai.v33i01.33014806
  • 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
  • Jiang, B., Li, C., De Rijke, M., Yao, X., & Chen, H. (2019). Probabilistic feature selection and classification vector machine. ACM Transactions on Knowledge Discovery from Data, 13(2), Article 21. https://doi.org/10.1145/3309541
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