Pessimistic Off-Policy Optimization for Learning to Rank

TL;DR

This paper introduces a pessimistic off-policy optimization method for learning to rank in recommender systems, addressing data imbalance and combinatorial action spaces by using confidence bounds to improve policy learning.

Contribution

It proposes a novel, computationally efficient pessimistic approach with Bayesian and frequentist variants, incorporating empirical Bayes to handle unknown priors, outperforming existing methods.

Findings

Outperforms inverse propensity score methods in experiments

Robust and general approach demonstrated empirically

Effective in addressing data imbalance in ranking tasks

Abstract

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 and is both robust and general.