Debiased Balanced Interleaving at Amazon Search
Nan Bi, Pablo Castells, Daniel Gilbert, Slava Galperin, Patrick Tardif, Sachin Ahuja · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Interleaving is an online evaluation technique that has shown to be orders of magnitude more sensitive than traditional A/B tests. It presents users with a single merged result of the compared rankings and then attributes user actions back to the evaluated rankers. Different interleaving methods in the literature have their advantages and limitations with respect to unbiasedness, sensitivity, preservation of user experience, and implementation and computation complexity. We propose a new interleaving method that utilizes a counterfactual evaluation framework for credit attribution while sticking to the simple ranking merge policy of balanced interleaving, and formally derive an unbiased estimator for comparing rankers with theoretical guarantees. We then confirm the effectiveness of our method with both synthetic and real experiments. We also discuss practical considerations of bringing different interleaving methods from the literature into a large-scale experiment, and show that our method achieves a favorable tradeoff in implementation and computation complexity while preserving statistical power and reliability. We have successfully implemented our method and produced consistent conclusions at the scale of billions of search queries. We report 10 online experiments that apply our method to e-commerce search, and observe a 60x sensitivity gain over A/B tests. We also find high correlations between our proposed estimator and corresponding A/B metrics, which helps interpret interleaving results in the magnitude of A/B measurements.