  Off-policy learning is a framework for optimizing policies without deploying
them, using data collected by another policy. In recommender systems, this is
especially challenging due to the imbalance in logged data: some items are
recommended and thus logged more frequently than others. This is further
perpetuated when recommending a list of items, as the action space is
combinatorial. To address this challenge, we study pessimistic off-policy
optimization for learning to rank. The key idea is to compute lower confidence
bounds on parameters of click models and then return the list with the highest
pessimistic estimate of its value. This approach is computationally efficient
and we analyze it. We study its Bayesian and frequentist variants, and overcome
the limitation of unknown prior by incorporating empirical Bayes. To show the
empirical effectiveness of our approach, we compare it to off-policy optimizers
that use inverse propensity scores or neglect uncertainty. Our approach
outperforms all baselines, is robust, and is also general.
