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Interactively optimizing information retrieval systems as a dueling bandits problem
Abstract
We present an on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems. In particular, we only require pairwise comparisons which were shown to be reliably inferred from implicit feedback (Joachims et al., 2007; Radlinski et al., 2008b). We will present an algorithm with theoretical guarantees as well as simulation results.
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Cited by
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Holzinger, A. (2016) · Brain Informatics · doi:10.1007/s40708-016-0042-6 · Cited by 906
Unbiased Learning-to-Rank with Biased Feedback
Joachims, T., Swaminathan, A., & Schnabel, T. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018699 · Cited by 531
Self-citationInteractive Intent Modeling for Exploratory Search
Ruotsalo, T., Peltonen, J., Eugster, M. J. A., Głowacka, D., Floréen, P., Myllymäki, P., Jacucci, G., & Kaski, S. (2018) · ACM Transactions on Information Systems · doi:10.1145/3231593 · Cited by 57
A Survey of Reinforcement Learning from Human Feedback
Kaufmann, T., Weng, P., Bengs, V., & Hüllermeier, E. (2023) · arXiv · doi:10.48550/arxiv.2312.14925 · Cited by 43
Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation
Sokolov, A., Riezler, S., & Urvoy, T. (2016) · arXiv · doi:10.48550/arxiv.1601.04468 · Cited by 14
Metadata
- Authors
- Yue, Y., & Joachims, T.
- Venue
- Proceedings of the 26th Annual International Conference on Machine Learning
- Publisher
- ACM
- Published
- 2009-06-14
- Pages
- 1201-1208
- OpenAlex
- W2009979684
- Cited by
- 268
- References
- 0
- FWCI
- 15.13
- Type
- conference paper
- Language
- en
- Open access
- closed
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