Personalization is a crucial aspect of many online experiences. In particular, content ranking is often a key component in delivering sophisticated personalization results. Commonly, supervised learning-to-rank methods are applied, which suffer from bias introduced during data collection by production systems in charge of producing the ranking. To compensate for this problem, we leverage contextual multi-armed bandits. We propose novel extensions of two well-known algorithms viz. LinUCB and Linear Thompson Sampling to the ranking use-case. To account for the biases in a production environment, we employ the position-based click model. Finally, we show the validity of the proposed algorithms by conducting extensive offline experiments on synthetic datasets as well as customer facing online A/B experiments.
@article{arxiv.2004.13106,
title = {Learning to Rank in the Position Based Model with Bandit Feedback},
author = {Beyza Ermis and Patrick Ernst and Yannik Stein and Giovanni Zappella},
journal= {arXiv preprint arXiv:2004.13106},
year = {2020}
}