Overcoming Prior Misspecification in Online Learning to Rank

Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:594-614, 2023.

Abstract

The recent literature on online learning to rank (LTR) has established the utility of prior knowledge to Bayesian ranking bandit algorithms. However, a major limitation of existing work is the requirement for the prior used by the algorithm to match the true prior. In this paper, we propose and analyze adaptive algorithms that address this issue and additionally extend these results to the linear and generalized linear models. We also consider scalar relevance feedback on top of click feedback. Moreover, we demonstrate the efficacy of our algorithms using both synthetic and real-world experiments.

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-azizi23a, title = {Overcoming Prior Misspecification in Online Learning to Rank}, author = {Azizi, Javad and Meshi, Ofer and Zoghi, Masrour and Karimzadehgan, Maryam}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {594--614}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/azizi23a/azizi23a.pdf}, url = {https://proceedings.mlr.press/v206/azizi23a.html}, abstract = {The recent literature on online learning to rank (LTR) has established the utility of prior knowledge to Bayesian ranking bandit algorithms. However, a major limitation of existing work is the requirement for the prior used by the algorithm to match the true prior. In this paper, we propose and analyze adaptive algorithms that address this issue and additionally extend these results to the linear and generalized linear models. We also consider scalar relevance feedback on top of click feedback. Moreover, we demonstrate the efficacy of our algorithms using both synthetic and real-world experiments.} }
Endnote
%0 Conference Paper %T Overcoming Prior Misspecification in Online Learning to Rank %A Javad Azizi %A Ofer Meshi %A Masrour Zoghi %A Maryam Karimzadehgan %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-azizi23a %I PMLR %P 594--614 %U https://proceedings.mlr.press/v206/azizi23a.html %V 206 %X The recent literature on online learning to rank (LTR) has established the utility of prior knowledge to Bayesian ranking bandit algorithms. However, a major limitation of existing work is the requirement for the prior used by the algorithm to match the true prior. In this paper, we propose and analyze adaptive algorithms that address this issue and additionally extend these results to the linear and generalized linear models. We also consider scalar relevance feedback on top of click feedback. Moreover, we demonstrate the efficacy of our algorithms using both synthetic and real-world experiments.
APA
Azizi, J., Meshi, O., Zoghi, M. & Karimzadehgan, M.. (2023). Overcoming Prior Misspecification in Online Learning to Rank. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:594-614 Available from https://proceedings.mlr.press/v206/azizi23a.html.

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