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Reinforcement learning for content's customization: a first step of experimentation in Skyscanner

Chiara Giachino (Department of Management, School of Management and Economics, University of Turin, Turin, Italy)
Luigi Bollani (ESOMAS Department, School of Management and Economics, University of Turin, Turin, Italy)
Alessandro Bonadonna (Department of Management, School of Management and Economics, University of Turin, Turin, Italy)
Marco Bertetti (Independent Reseacher, London, UK)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 15 January 2021

Issue publication date: 7 June 2021

325

Abstract

Purpose

The aim of the paper is to test and demonstrate the potential benefits in applying reinforcement learning instead of traditional methods to optimize the content of a company's mobile application to best help travellers finding their ideal flights. To this end, two approaches were considered and compared via simulation: standard randomized experiments or A/B testing and multi-armed bandits.

Design/methodology/approach

The simulation of the two approaches to optimize the content of its mobile application and, consequently, increase flights conversions is illustrated as applied by Skyscanner, using R software.

Findings

The first results are about the comparison between the two approaches – A/B testing and multi-armed bandits – to identify the best one to achieve better results for the company. The second one is to gain experiences and suggestion in the application of the two approaches useful for other industries/companies.

Research limitations/implications

The case study demonstrated, via simulation, the potential benefits to apply the reinforcement learning in a company. Finally, the multi-armed bandit was implemented in the company, but the period of the available data was limited, and due to its strategic relevance, the company cannot show all the findings.

Practical implications

The right algorithm can change according to the situation and industry but would bring great benefits to the company's ability to surface content that is more relevant to users and help improving the experience for travellers. The study shows how to manage complexity and data to achieve good results.

Originality/value

The paper describes the approach used by an European leading company operating in the travel sector in understanding how to adapt reinforcement learning to its strategic goals. It presents a real case study and the simulation of the application of A/B testing and multi-armed bandit in Skyscanner; moreover, it highlights practical suggestion useful to other companies.

Keywords

Acknowledgements

A special thanks for the precious help to Neal Lathia (Data Scientist), Julianna Gobolos-Szabo (Engineer), Istvan Meszaros (Engineer), Akos Toth-Mate (Engineer).

Citation

Giachino, C., Bollani, L., Bonadonna, A. and Bertetti, M. (2021), "Reinforcement learning for content's customization: a first step of experimentation in Skyscanner", Industrial Management & Data Systems, Vol. 121 No. 6, pp. 1417-1434. https://doi.org/10.1108/IMDS-12-2019-0722

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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