Regression Tree for Bandits Models in A/B Testing

被引：3

作者：

Claeys, Emmanuelle ^{[1
,2
,3
]}

Gancarski, Pierre ^{[1
]}

Maumy-Bertrand, Myriam ^{[2
]}

Wassner, Hubert ^{[3
]}

机构：

[1] Strasbourg Univ, CNRS, ICUBE, 300 Bd Sebastien Brant, F-67400 Illkirch Graffenstaden, France

[2] Strasbourg Univ, CNRS, IRMA, 7 Rue Rene Descartes, F-67000 Strasbourg, France

[3] AB TASTY, 3 Impasse Planchette, F-75003 Paris, France

来源：

ADVANCES IN INTELLIGENT DATA ANALYSIS XVI, IDA 2017 | 2017年 / 10584卷

关键词：

A/B Test; best arm identification; bandit models; regression tree; time clustering; topic modeling;

D O I：

10.1007/978-3-319-68765-0_5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the context of Web A/B testing, dynamic assignment of traffic aims to promote the best variation (A or B) as quickly as possible. However, dynamic assignment is difficult to use when the difference between A and B affects the visitor differently according to his/her personal characteristics and his/her history (number of visits, navigation on the website ... ). In this paper, we propose a dynamic assignment strategy based on a visitor segmentation determined automatically from the visitors navigation and characteristics.

引用

页码：52 / 62

页数：11

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