Implications and New Directions for IR Research and Practices
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-23229-9_7
Reference70 articles.
1. Agarwal, A., Wang, X., Li, C., Bendersky, M., & Najork, M. (2019). Addressing trust bias for unbiased learning-to-rank. In The world wide web conference (pp. 4–14). ACM. https://doi.org/10.1145/3308558.3313697
2. Ai, Q., Bi, K., Luo, C., Guo, J., & Croft, W. B. (2018). Unbiased learning to rank with unbiased propensity estimation. In The 41st international ACM SIGIR conference on research & development in information retrieval (pp. 385–394). ACM. https://doi.org/10.1145/3209978.3209986
3. Ai, Q., Yang, T., Wang, H., & Mao, J. (2021). Unbiased learning to rank: Online or offline? ACM Transactions on Information Systems (TOIS), 39(2), 1–29. https://doi.org/10.1145/3439861
4. Azzopardi, L. (2021). Cognitive biases in search: A review and reflection of cognitive biases in information retrieval. In Proceedings of the 2021 ACM SIGIR conference on human information interaction and retrieval (pp. 27–37). ACM. https://doi.org/10.1145/3406522.3446023
5. Büschel, W., Mitschick, A., & Dachselt, R. (2018). Here and now: Reality-based information retrieval: Perspective paper. In Proceedings of the 2018 ACM SIGIR conference on human information interaction & retrieval (pp. 171–180). https://doi.org/10.1145/3176349.3176384
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