Recommender Systems and Supplier Competition on Platforms

30 Pages Posted: 24 Mar 2022 Last revised: 15 Jun 2022

See all articles by Amelia Fletcher

Amelia Fletcher

Centre for Competition Policy and Norwich Business School, UEA

Peter L. Ormosi

Norwich Business School; University of East Anglia (UEA) - Centre for Competition Policy

Rahul Savani

Dept. of Computer Science, University of Liverpool

Date Written: June 14, 2022

Abstract

An increasing number of consumption decisions happen on two (or more) sided platforms that carry goods and services from a large number of suppliers. In principle, such easy access to a multiplicity of products should lower consumers' search costs and thereby enhance their decision-making. In practice, consumers can struggle with such a large choice set. Recommender systems (RS) are designed to solve this problem by suggesting relevant products based on a consumer's preferences. This can be good both for consumers and for effective competition between suppliers. However, as has been amply shown within the computer science literature, RS do not necessarily have the ability or incentive to carry out this role perfectly. Even if RS are intended to be consumer-centric, they tend to exhibit inherent biases in the recommendations made. These are associated with the choice of RS model design, the data that feeds into the RS model, and feedback loops between these two elements. The key contribution of this paper is in highlighting that, since these biases can be expected to change consumption decisions, they can in turn distort competition between suppliers, potentially creating barriers to entry and expansion, increasing concentration, and reducing variety and innovation. This can happen even in the absence of any malicious intent from the platform, but the situation may be worsened if a platform's own interests diverge from those of consumers and this is reflected in the RS design. This paper identifies and outlines these important effects at a high level and in doing so provides a trigger for future research.

Keywords: recommender systems, artificial intelligence, entry barriers, algorithmic bias, trustworthy autonomous systems

Suggested Citation

Fletcher, Amelia and Ormosi, Peter L. and Ormosi, Peter L. and Savani, Rahul, Recommender Systems and Supplier Competition on Platforms (June 14, 2022). Available at SSRN: https://ssrn.com/abstract=4036813 or http://dx.doi.org/10.2139/ssrn.4036813

Amelia Fletcher

Centre for Competition Policy and Norwich Business School, UEA ( email )

Norwich
United Kingdom

HOME PAGE: http://https://people.uea.ac.uk/amelia_fletcher

Peter L. Ormosi (Contact Author)

Norwich Business School ( email )

Norwich
NR4 7TJ
United Kingdom

University of East Anglia (UEA) - Centre for Competition Policy ( email )

UEA
Norwich Research Park
Norwich, Norfolk NR47TJ
United Kingdom

Rahul Savani

Dept. of Computer Science, University of Liverpool ( email )

Department of Computer Science
Liverpool, L69 3BX
Great Britain

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