A/B Testing Measurement Framework for Recommendation Models Based on Expected Revenue

Meisam Hejazinia, Majid Hosseini, B. Sih
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Abstract

We provide a method to determine whether a new recommendation system improves the revenue per visit (RPV) compared to the status quo. We achieve our goal by splitting RPV into conversion rate and average order value (AOV). We use the two-part test suggested by Lachenbruch to determine if the data generating process in the new system is different. In cases that this test does not give us a definitive answer about the change in RPV, we propose two alternative tests to determine if RPV has changed. Both of these tests rely on the assumption that non-zero purchase values follow a log-normal distribution. We empirically validate this assumption using data collected at different points in time from this http URL. On average, our method needs a smaller sample size than other methods. Furthermore, it does not require any subjective outlier removal. Finally, it characterizes the uncertainty around RPV by providing a confidence interval.
基于预期收益的推荐模型A/B测试度量框架
我们提供了一种方法来确定与现状相比,新的推荐系统是否提高了每次访问的收益(RPV)。我们通过将RPV分解为转化率和平均订单值(AOV)来实现我们的目标。我们使用Lachenbruch建议的两部分测试来确定新系统中的数据生成过程是否不同。如果这个测试不能给我们一个关于RPV变化的明确答案,我们提出两个替代测试来确定RPV是否发生了变化。这两种检验都依赖于非零购买值遵循对数正态分布的假设。我们使用从这个http URL在不同时间点收集的数据来验证这个假设。平均而言,我们的方法需要比其他方法更小的样本量。此外,它不需要任何主观的异常值去除。最后,它通过提供置信区间来表征RPV的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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