Offline A/B testing for Recommender Systems (1801.07030v1)

Abstract: Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data. We focus on evaluation methods that compute an estimator of the potential uplift in revenue that could generate this new technology. It helps to iterate faster and to avoid losing money by detecting poor policies. These estimators are known as counterfactual or off-policy estimators. We show that traditional counterfactual estimators such as capped importance sampling and normalised importance sampling are experimentally not having satisfying bias-variance compromises in the context of personalised product recommendation for online advertising. We propose two variants of counterfactual estimates with different modelling of the bias that prove to be accurate in real-world conditions. We provide a benchmark of these estimators by showing their correlation with business metrics observed by running online A/B tests on a commercial recommender system.

• The paper proposes novel piecewise and pointwise Normalised Capped Importance Sampling estimators to better manage bias-variance trade-offs in recommender system evaluations.
• It critiques standard counterfactual methods like BIS, CIS, and NIS, demonstrating how traditional techniques fall short in addressing high variance and capping-induced biases.
• Empirical tests show that the proposed estimators achieve superior correlation with online A/B testing metrics, reducing false negatives and enhancing risk-averse decision-making.

Offline A/B Testing for Recommender Systems

The paper "Offline A/B Testing for Recommender Systems" addresses the intricate challenges and methodologies associated with evaluating recommender systems in an offline setting before deploying potential changes online. Online A/B testing, while accurate, is resource-intensive and methodologically demanding, necessitating surrogate methodologies that can promise similar levels of reliability yet more efficiently.

Background and Motivation

Recommender systems fundamentally drive the engagement and revenue metrics of online platforms through personalized content delivery. The inherent challenge lies in evaluating new recommender algorithms for their potential uplift in performance without exposing the entire user base to unproven and potentially detrimental systems. Traditional A/B testing, though robust, requires significant time investment, computational resources, and careful rollout strategies to mitigate risk. Hence, the need arises for offline estimations—counterfactual or off-policy evaluations—that accurately predict the performance of new systems with reduced operational overheads.

Evaluation Techniques and Contributions

The paper critiques traditional counterfactual estimators like Basic Importance Sampling (BIS), Capped Importance Sampling (CIS), and Normalised Importance Sampling (NIS), pointing out their unsatisfactory bias-variance trade-offs in the context of personalized recommendations. BIS, for example, although unbiased, suffers from high variance due to the magnitude and dimensionality of its action space. CIS, which attempts to tackle this through weight capping, introduces new biases dependent on the capping settings.

To address these shortcomings, the authors propose two novel variants of counterfactual estimation aimed at better bias-variance management. The first uses piecewise normalization, while the second incorporates a pointwise normalization technique, both seeking to fine-tune the estimation at a more granular level than the global approaches previously considered.

Empirical Findings

A robust analysis is carried out by benchmarking various estimators against real-world online A/B test results. The paper empirically shows that the proposed piecewise and pointwise Normalised Capped Importance Sampling (NCIS) estimators demonstrate superior correlation with actual business metrics compared to existing methods. Particularly, these estimators exhibit fewer false negatives, a critical insight for practical applications where operational performance improvements may otherwise be overlooked.

Implications and Future Directions

This paper has crucial implications for how practitioners in the field approach the prototyping and evaluation phase of recommender system implementations. The refined estimators provide a more reliable foundation underpinning decision-making regarding new recommender deployments. In a broader sense, this research offers a pathway for future studies to explore variance reduction techniques in counterfactual analysis, possibly integrating adaptive or dynamic capping strategies.

Looking forward, advancements in AI, particularly in reinforcement learning contexts, could further enhance these evaluations—balancing exploration and exploitation opportunities in online systems. Future investigations might also explore leveraging hybrid models that account for non-linear interactions prevalent in user behavior data sets and that offer more dynamic adaptability across diverse application scenarios.

In summation, this paper's contribution lies in advancing the methodological toolkit available for the offline evaluation of recommender systems, thereby aiding practitioners to make data-driven and risk-averse deployment decisions.