01 Apr 2022 - 21 Mar 2023
01 Apr 2022 - 21 Mar 2023
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. This work will look at how these biases in the RS impact market competition between suppliers of products on the RS platform.
The first work package of our project designs a simulation framework that incorporates boundedly rational consumers on the downstream side of the RS platform, and firms competing in horizontally and vertically differentiated goods markets on the upstream side. Through these simulations we aim to demonstrate that, since RS 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.
To put this in the context of an actual market, in our second work package, we turn to a specific example of two-sided markets, music streaming platforms, in which autonomous RS are deployed to map out and influence consumer preferences on one side. Our key research objective is to understand whether and how these RS affect the suppliers of music content (something that was alluded to in a 2021 House of Commons’ inquiry into streaming platforms). We will achieve this by running an experiment with observational data from streaming platforms. Finally, we will offer a normative discussion on the types of regulations and interventions needed to address the potential limitations of competition. Our evidence is aimed to contribute to the current policy debate on the competition of music streaming platforms.
The impact of autonomous recommender systems on competition between suppliers on a platform
(Pre print) November 2022 | Recommender Systems and Supplier Competition on Platforms
Professor of Competition Economics, University of East Anglia
Professor of Computer Science, University of Liverpool
Professor of Computer Science, University of Liverpool
Lecturer in Media and Communication, University of London
Lecturer in Competition Law and IP, University of East Anglia
Professor of Computer Science, King’s College London
Professor of Competition Policy, University of East Anglia
Associate Professor in IP/IT/Media Law, University of East Anglia
