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arXiv · 2007.12719

Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking

Harrie Oosterhuis·Maarten de Rijke
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Abstract

Counterfactual evaluation can estimate Click-Through-Rate (CTR) differences between ranking systems based on historical interaction data, while mitigating the effect of position bias and item-selection bias. We introduce the novel Logging-Policy Optimization Algorithm (LogOpt), which optimizes the policy for logging data so that the counterfactual estimate has minimal variance. As minimizing variance leads to faster convergence, LogOpt increases the data-efficiency of counterfactual estimation. LogOpt turns the counterfactual approach - which is indifferent to the logging policy - into an online approach, where the algorithm decides what rankings to display. We prove that, as an online evaluation method, LogOpt is unbiased w.r.t. position and item-selection bias, unlike existing interleaving methods. Furthermore, we perform large-scale experiments by simulating comparisons between thousands of rankers. Our results show that while interleaving methods make systematic errors, LogOpt is as efficient as interleaving without being biased.

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Harrie Oosterhuis, Maarten de Rijke. 2020-07-24. Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. https://doi.org/10.1145/3409256.3409820

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