Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining
Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining
复制标题
两者皆有——从 A/B 测试到 A
DOI：
10.1007/978-3-319-71273-4_10
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Mykola Pechenizkiy
中科院分区：
文献类型：
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作者：
W. Duivesteijn;T. Farzami;Thijs Putman;E. Peer;Hilde J. P. Weerts;Jasper N. Adegeest;Gerson Foks;Mykola Pechenizkiy
在传统的A/B测试中，我们有同一产品的两个变体，一组测试对象和一个成功的衡量标准。在一个随机实验中，每个测试对象都有两种变体中的一种，并且对每个变体的成功度量进行汇总。与最成功相关的产品变体被保留，而另一个变体被丢弃。然而，这假定生产产品的公司只有足够的能力维持两种产品变体中的一种。如果有更多的可用容量，那么先进的数据科学技术可以从A/B测试结果中为公司提取更多的利润。例外模型挖掘就是这样一种先进的数据科学技术，它专门用于识别与总体人群行为不同的子群体。使用EMM的关联模型类，我们可以发现一般种群更喜欢变体B的亚种群更喜欢变体A，反之亦然。这种数据科学技术应用于StudyPortals的数据，StudyPortals是一个全球研究选择平台，对其网站的设计进行了a /B测试。
In traditional A/B testing, we have two variants of the same product, a pool of test subjects, and a measure of success. In a randomized experiment, each test subject is presented with one of the two variants, and the measure of success is aggregated per variant. The variant of the product associated with the most success is retained, while the other variant is discarded. This, however, presumes that the company producing the products only has enough capacity to maintain one of the two product variants. If more capacity is available, then advanced data science techniques can extract more profit for the company from the A/B testing results. Exceptional Model Mining is one such advanced data science technique, which specializes in identifying subgroups that behave differently from the overall population. Using the association model class for EMM, we can find subpopulations that prefer variant A where the general population prefers variant B, and vice versa. This data science technique is applied on data from StudyPortals, a global study choice platform that ran an A/B test on the design of aspects of their website.
DOI：
10.1145/2939672.2939752
发表时间：
2016-08
期刊：
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
影响因子：
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作者：
F. Lemmerich;Martin Becker;Philipp Singer;D. Helic;A. Hotho;M. Strohmaier
通讯作者：
F. Lemmerich;Martin Becker;Philipp Singer;D. Helic;A. Hotho;M. Strohmaier