平台上的推荐系统和供应商竞争

IF 1.3 4区 社会学 Q3 ECONOMICS
Amelia Fletcher, Peter L Ormosi, Rahul Savani
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引用次数: 0

摘要

数字平台可以在一个地方提供多种项目。原则上,这应该会降低最终用户的搜索成本,改善他们的决策,从而增强使用该平台的供应商之间的竞争。但最终用户却难以应对庞大的选择集。推荐系统(RSs)可以通过预测最终用户的偏好并推荐相关产品来提供帮助。然而,这一预测过程可能会在所提出的建议中产生系统性偏差,包括人气偏差、在位性偏差、同质性偏差和从众性偏差。这些偏差的性质和程度将取决于RS模型设计的选择、输入RS模型的数据以及这两个元素之间的反馈循环。我们讨论了这些系统性偏差如何可能会恶化最终用户的选择和损害供应商之间的竞争。它们可以增加集中度、进入和扩张的障碍、市场细分和价格,同时减少品种和创新。甚至当平台的利益与终端用户的利益大体一致时,这种情况也会发生,而当这些动机出现分歧时,情况可能会变得更糟。我们在高水平上概述了这些重要影响,目的是突出出现的竞争问题,包括政策影响,并激励未来的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recommender Systems and Supplier Competition on Platforms
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.
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来源期刊
CiteScore
2.20
自引率
26.70%
发文量
16
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