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

Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model