Recommender systems and supplier competition on platforms

Fletcher, Amelia, Ormosi, Peter L. ORCID: https://orcid.org/0000-0001-6472-6511 and Savani, Rahul (2023) Recommender systems and supplier competition on platforms. Journal of Competition Law and Economics, 19 (3). pp. 397-426. ISSN 1744-6414

Preview

PDF (nhad009) - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (753kB) | Preview

Abstract

Digital platforms can offer a multiplicity of items in one place. This should, in principle, lower end-users’ search costs and improve their decision-making, and thus enhance competition between suppliers using the platform. But end-users struggle with large choice sets. Recommender systems (RSs) can help by predicting end-users’ preferences and suggesting relevant products. However, this process of prediction can generate systemic biases in the recommendations made, including popularity bias, incumbency bias, homogeneity bias, and conformity bias. The nature and extent of these biases will depend on the choice of RS model design, the data feeding into the RS model, and feedback loops between these two elements. We discuss how these systemic biases might be expected to worsen end-user choices and harm competition between suppliers. They can increase concentration, barriers to entry and expansion, market segmentation, and prices while reducing variety and innovation. This can happen even when a platform’s interests are broadly aligned with those of end-users, and the situation may be worsened where these incentives diverge. We outline these important effects at a high level, with the objective to highlight the competition issues arising, including policy implications, and to motivate future research.

Item Type:	Article
Additional Information:	Funding Information: This work has benefited from a UKRI Trustworthy Autonomous Systems (TAS) Pump Priming grant EP/V00784X/1. The UKRI TAS Hub assembles a team from the Universities of Southampton, Nottingham and King’s College London. The Hub sits at the centre of the £33M Trustworthy Autonomous Systems Programme, funded by the UKRI Strategic Priorities Fund. Amelia Fletcher’s work was part-funded by EPSRC grant EP/T022493/1.
Uncontrolled Keywords:	algorithmic bias,digital platforms,entry barriers,recommender systems,trustworthy autonomous systems,economics and econometrics,law ,/dk/atira/pure/subjectarea/asjc/2000/2002
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Responsible Business Regulation Group Faculty of Social Sciences > Research Centres > Centre for Competition Policy
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	25 Apr 2024 15:31
Last Modified:	02 May 2024 08:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/95021
DOI:	10.1093/joclec/nhad009

Downloads

Actions (login required)

View Item