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

• URL: http://arxiv.org/abs/2007.12719v2
• Date: Tue, 28 Jul 2020 17:59:33 GMT
• Title: Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking
• Authors: Harrie Oosterhuis and Maarten de Rijke
• Abstract summary: We introduce the novel Logging-Policy Optimization Algorithm (LogOpt), which optimize the policy for logging data. 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.
• Score: 74.46448041224247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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