AB Testing for Process Versions with Contextual Multi-armed Bandit Algorithms View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-05-17

AUTHORS

Suhrid Satyal , Ingo Weber , Hye-young Paik , Claudio Di Ciccio , Jan Mendling

ABSTRACT

Business process improvement ideas can be validated through sequential experiment techniques like AB Testing. Such approaches have the inherent risk of exposing customers to an inferior process version, which is why the inferior version should be discarded as quickly as possible. In this paper, we propose a contextual multi-armed bandit algorithm that can observe the performance of process versions and dynamically adjust the routing policy so that the customers are directed to the version that can best serve them. Our algorithm learns the best routing policy in the presence of complications such as multiple process performance indicators, delays in indicator observation, incomplete or partial observations, and contextual factors. We also propose a pluggable architecture that supports such routing algorithms. We evaluate our approach with a case study. Furthermore, we demonstrate that our approach identifies the best routing policy given the process performance and that it scales horizontally. More... »

PAGES

19-34

Book

TITLE

Advanced Information Systems Engineering

ISBN

978-3-319-91562-3
978-3-319-91563-0

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-91563-0_2

DOI

http://dx.doi.org/10.1007/978-3-319-91563-0_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104015578


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