Have it both ways : from A/B testing to A&B testing with exceptional model mining

作者: Wouter Duivesteijn , Tara Farzami , Thijs Putman , Evertjan Peer , Hilde J. P. Weerts

DOI: 10.1007/978-3-319-71273-4_10

关键词: Artificial intelligenceClass (philosophy)Randomized experimentMachine learningProduct (category theory)Computer scienceMeasure (data warehouse)Test (assessment)PopulationAssociation (object-oriented programming)A/B testing

摘要: In traditional A/B testing, we have two variants of the same product, a pool test subjects, and measure success. randomized experiment, each subject is presented with one variants, success aggregated per variant. The variant product associated most retained, while other discarded. This, however, presumes that company producing products only has enough capacity to maintain variants. If more available, then advanced data science techniques can extract profit for from testing results. Exceptional Model Mining such technique, which specializes in identifying subgroups behave differently overall population. Using association model class EMM, find subpopulations prefer A where general population prefers B, vice versa. This technique applied on StudyPortals, global study choice platform ran an design aspects their website.

参考文章(26)
Sandy Moens, Mario Boley, Instant Exceptional Model Mining Using Weighted Controlled Pattern Sampling intelligent data analysis. ,vol. 8819, pp. 203- 214 ,(2014) , 10.1007/978-3-319-12571-8_18
Joseph Jay Williams, Na Li, Juho Kim, Jacob Whitehill, Samuel Maldonado, Mykola Pechenizkiy, Larry Chu, Neil Heffernan, The MOOClet Framework: Improving Online Education through Experimentation and Personalization of Modules Social Science Research Network. ,(2014) , 10.2139/SSRN.2523265
Stefan Wrobel, An Algorithm for Multi-relational Discovery of Subgroups european conference on principles of data mining and knowledge discovery. pp. 78- 87 ,(1997) , 10.1007/3-540-63223-9_108
Willi Klösgen, Knowledge Discovery in Databases and Data Mining international syposium on methodologies for intelligent systems. pp. 623- 632 ,(1996) , 10.1007/3-540-61286-6_186
Jon Kleinberg, Christos Papadimitriou, Prabhakar Raghavan, A Microeconomic View of Data Mining Data Mining and Knowledge Discovery. ,vol. 2, pp. 311- 324 ,(1998) , 10.1023/A:1009726428407
Dennis Leman, Ad Feelders, Arno Knobbe, Exceptional Model Mining european conference on machine learning. pp. 1- 16 ,(2008) , 10.1007/978-3-540-87481-2_1
David J. Hand, Pattern Detection and Discovery Lecture Notes in Computer Science. pp. 1- 12 ,(2002) , 10.1007/3-540-45728-3_1
Wouter Duivesteijn, Ad Feelders, Arno Knobbe, Different slopes for different folks Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12. pp. 868- 876 ,(2012) , 10.1145/2339530.2339668
Piotr Rzepakowski, Szymon Jaroszewicz, Decision trees for uplift modeling with single and multiple treatments Knowledge and Information Systems. ,vol. 32, pp. 303- 327 ,(2012) , 10.1007/S10115-011-0434-0