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Results: 7
Number of items: 7
  • Open Access
    Kersbergen, B., Sprangers, O., Kootte, F., Guha, S., de Rijke, M., & Schelter, S. (2024). Etude - Evaluating the Inference Latency of Session-Based Recommendation Models at Scale. In 2024 IEEE 40th International Conference on Data Engineering: ICDE 2024 : 13-17 May 2024, Utrecht, Netherlands : proceedings (pp. 5177-5183). IEEE Computer Society. https://doi.org/10.1109/icde60146.2024.00389
  • Open Access
    Deng, S., Sprangers, O., Li, M., Schelter, S., & de Rijke, M. (2024). Domain Generalization in Time Series Forecasting. ACM Transactions on Knowledge Discovery from Data, 18(5), Article 113. https://doi.org/10.1145/3643035
  • Open Access
    Sprangers, O., Wadman, W., Schelter, S., & de Rijke, M. (2024). Hierarchical forecasting at scale. International Journal of Forecasting, 40(4), 1689-1700. https://doi.org/10.1016/j.ijforecast.2024.02.006
  • Open Access
    Sprangers, O. R. (2024). Efficient and accurate forecasting in large-scale settings. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Sprangers, O., Schelter, S., & de Rijke, M. (2023). Parameter Efficient Deep Probabilistic Forecasting. International Journal of Forecasting, 39(1), 332-345. https://doi.org/10.1016/j.ijforecast.2021.11.011
  • Kersbergen, B., Sprangers, O., & Schelter, S. (2022). Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale. In SIGMOD '22: proceedings of the 2022 International Conference on the Management of Data : June 12-17, 2022, Philadelphia, PA, USA (pp. 150-159). Association for Computing Machinery. https://doi.org/10.1145/3514221.3517901
  • Sprangers, O., Schelter, S., & de Rijke, M. (2021). Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression. In KDD ’21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining : August 14-18, 2021, virtual event, Singapore (pp. 1510-1520). Association for Computing Machinery. https://doi.org/10.1145/3447548.3467278
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