Off-policy evaluation for slate recommendation (1605.04812v3)

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.

• The paper presents the pseudoinverse estimator for off-policy evaluation, dramatically reducing data requirements compared to traditional IPS methods.
• It leverages a linearity assumption on slate-level rewards and demonstrates improved performance through experiments on real-world datasets.
• The estimator facilitates learning-to-rank tasks by enabling effective off-policy optimization under relaxed and robust conditions.

Off-Policy Evaluation for Slate Recommendation

The paper "Off-policy evaluation for slate recommendation" addresses a critical challenge in the domain of recommender systems, namely the off-policy evaluation of recommendation algorithms that output ordered sets or lists of items. Such scenarios are increasingly prevalent in web search, advertising, and various recommendation systems. The authors propose a novel estimator, inspired by techniques from combinatorial bandits, to evaluate the performance of a new policy using pre-existing logged data. Notably, this estimator claims to achieve unbiased estimates under more relaxed conditions than previously considered methods and is shown through empirical evaluation to require substantially less data compared to conventional unbiased techniques.

Contribution Highlights

1. Pseudoinverse Estimator (PI): The primary contribution is the development of the pseudoinverse estimator for off-policy evaluation in settings where a policy recommends $\ell$ items from a pool of $m$. The estimator leans on a linearity assumption regarding slate-level rewards and reduces data requirements exponentially compared to inverse propensity score (IPS) methods. The paper mathematically demonstrates that PI can achieve an error bounded by $\mathcal{O}(\ell m/^2)$ under specific conditions, a significant improvement over traditional approaches requiring $m^{\Omega(\ell)}$ samples.
2. Empirical Verification: The authors conduct rigorous empirical experiments utilizing real-world search ranking datasets to validate their claims. The PI estimator consistently demonstrates superior performance across a variety of conditions, significantly outperforming baseline methodologies such as IPS and direct modeling approaches.
3. Off-Policy Optimization: The paper also explores an application of their estimation technique in learning to rank (L2R) tasks. By using the PI estimator to impute rewards at the action level, the authors enable standard ranking optimizations that do not require pointwise feedback, simplifying the process of policy optimization and acceleration.

Theoretical Underpinnings and Assumptions

The estimator is founded on two primary assumptions. First, a linearity assumption posits that slate-level rewards decompose additively across actions, thus simplifying the evaluation model. Second, the estimator assumes absolute continuity, meaning the logged policy must adequately cover the action space of the policy under evaluation. These assumptions allow the PI estimator to achieve unbiased evaluations and ameliorate the variance issues faced by IPS methods, providing a theoretical guarantee of efficiency.

The estimator's framework is a direct extension of combinatorial bandits, adapted for the off-policy context. The analysis provided within the paper offers distribution-dependent bounds and leverages variance analysis to illustrate the estimator's efficacy under varied circumstances.

Implications and Future Directions

The findings of this paper carry significant implications for both the theoretical understanding and practical deployment of slate recommendation systems:

• Practical Efficiency: The reduction in data requirements is particularly relevant for real-world applications where logging extensive interactions may be infeasible due to privacy, storage, or computational constraints.
• Robustness: The weaker conditions required for unbiased evaluation with the PI estimator introduce robustness, offering more reliable guidance for decisions in dynamic recommendation environments.
• Extension Potential: While the present work focuses on linear decompositions of rewards, future research could extend these principles to higher-dimensional interactions within slates, supporting more complex metrics that consider cross-item interactions.

The research represents a meaningful advance in off-policy evaluation and optimization for slate recommendation systems. Its integration of combinatorial bandit insights with large-scale data-driven policy evaluation highlights a sophisticated, yet applicable methodology worthy of further exploration in the broader AI and machine learning landscape.