Off-policy evaluation for slate recommendation

TL;DR

This paper introduces a new estimator for off-policy evaluation in slate recommendation systems, demonstrating improved accuracy, lower bias, and data efficiency through theoretical analysis and empirical validation on real-world data.

Contribution

We develop a practical, less biased estimator for slate policy evaluation that outperforms prior methods in accuracy and data efficiency, supported by theoretical guarantees and experiments.

Findings

Estimator is accurate across various settings

Achieves smaller bias than parametric approaches

Requires exponentially less data than previous estimators

Abstract

This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context---a common scenario in web search, ads, and recommendation. We build on techniques from combinatorial bandits to introduce a new practical estimator that uses logged data to estimate a policy's performance. A thorough empirical evaluation on real-world data reveals that our estimator is accurate in a variety of settings, including as a subroutine in a learning-to-rank task, where it achieves competitive performance. We derive conditions under which our estimator is unbiased---these conditions are weaker than prior heuristics for slate evaluation---and experimentally demonstrate a smaller bias than parametric approaches, even when these conditions are violated. Finally, our theory and experiments also show exponential savings in the amount of required data compared with general unbiased estimators.