  Counterfactual learning to rank (CLTR) relies on exposure-based inverse
propensity scoring (IPS), a LTR-specific adaptation of IPS to correct for
position bias. While IPS can provide unbiased and consistent estimates, it
often suffers from high variance. Especially when little click data is
available, this variance can cause CLTR to learn sub-optimal ranking behavior.
Consequently, existing CLTR methods bring significant risks with them, as
naively deploying their models can result in very negative user experiences. We
introduce a novel risk-aware CLTR method with theoretical guarantees for safe
deployment. We apply a novel exposure-based concept of risk regularization to
IPS estimation for LTR. Our risk regularization penalizes the mismatch between
the ranking behavior of a learned model and a given safe model. Thereby, it
ensures that learned ranking models stay close to a trusted model, when there
is high uncertainty in IPS estimation, which greatly reduces the risks during
deployment. Our experimental results demonstrate the efficacy of our proposed
method, which is effective at avoiding initial periods of bad performance when
little data is available, while also maintaining high performance at
convergence. For the CLTR field, our novel exposure-based risk minimization
method enables practitioners to adopt CLTR methods in a safer manner that
mitigates many of the risks attached to previous methods.
