  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.
