  A critical need for industrial recommender systems is the ability to evaluate
recommendation policies offline, before deploying them to production.
Unfortunately, widely used off-policy evaluation methods either make strong
assumptions about how users behave that can lead to excessive bias, or they
make fewer assumptions and suffer from large variance. We tackle this problem
by developing a new estimator that mitigates the problems of the two most
popular off-policy estimators for rankings, namely the position-based model and
the item-position model. In particular, the new estimator, called INTERPOL,
addresses the bias of a potentially misspecified position-based model, while
providing an adaptable bias-variance trade-off compared to the item-position
model. We provide theoretical arguments as well as empirical results that
highlight the performance of our novel estimation approach.
