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Results: 9
Number of items: 9
  • Open Access
    Zhao, Y. (2025). From enhancement to exploitation: The dual role of LLMs in recommender systems. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Gupta, S., Hager, P., Huang, J., Vardasbi, A., & Oosterhuis, H. (2024). Unbiased Learning to Rank: On Recent Advances and Practical Applications. In WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining : March 4-8, 2024, Merida, Mexico (pp. 1118–1121). Association for Computing Machinery. https://doi.org/10.1145/3616855.3636451
  • Open Access
    Huang, J., Oosterhuis, H., Mansoury, M., van Hoof, H., & de Rijke, M. (2024). Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. In SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 14-18, 2024, Washington, DC, USA (pp. 416-426). Association for Computing Machinery. https://doi.org/10.1145/3626772.3657749
  • Open Access
    Azzopardi, L., Clarke, C. L. A., Kantor, P., Mitra, B., Trippas, J. R., Ren, Z., Aliannejadi, M., Arabzadeh, N., Chandrasekar, R., de Rijke, M., Eustratiadis, P., Hersh, W., Huang, J., Kanoulas, E., Kareem, J., Li, Y., Lupart, S., Mekonnen, K. A., Roegiest, A., ... Zhao, Y. (2024). Report on the Search Futures Workshop at ECIR 2024. SIGIR Forum, 58(1). https://doi.org/10.1145/3687273.3687288
  • Open Access
    Huang, J. (2024). Learning recommender systems from biased user interactions. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Gupta, S., Hager, P., Huang, J., Vardasbi, A., & Oosterhuis, H. (2023). Recent Advances in the Foundations and Applications of Unbiased Learning to Rank. In SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 23-27, 2023, Taipei, Taiwan (pp. 3440–3443). Association for Computing Machinery. https://doi.org/10.1145/3539618.3594247
  • Open Access
    Huang, J., Oosterhuis, H., Cetinkaya, B., Rood, T., & de Rijke, M. (2022). State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. In SIGIR '22: proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 11-15, 2022, Madrid, Spain (pp. 2738-2748). The Association for Computing Machinery. https://doi.org/10.1145/3477495.3531716
  • Open Access
    Huang, J., Oosterhuis, H., & de Rijke, M. (2022). It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic. In WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining : February 21-25, 2022 : virtual event, Tempe, AZ, USA (pp. 381–389). Association for Computing Machinery. https://doi.org/10.1145/3488560.3498375
  • Open Access
    Huang, J., Oosterhuis, H., de Rijke, M., & van Hoof, H. (2020). Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. In RECSYS 2020: 14th ACM Conference on Recommender Systems : Virtual Event, Brazil, September 22-26, 2020 (pp. 190–199). The Association for Computing Machinery. https://doi.org/10.1145/3383313.3412252
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