Off-policy evaluation for slate recommendation
This addresses the challenge of off-policy evaluation for slate recommendations in web search, ads, and recommendation systems, offering a practical solution with weaker assumptions and improved efficiency.
The paper tackles the problem of evaluating recommendation policies that output ordered sets of items, introducing a new estimator that uses logged data to estimate policy performance. It shows the estimator is accurate in real-world settings, achieves competitive performance in learning-to-rank tasks, and provides exponential data savings compared to general unbiased estimators.
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.