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Algorithmic recommendations mediate interactions between millions ofcustomers and products (in turn, their producers and sellers) on largee-commerce marketplaces like Amazon. In recent years, the producers and sellershave raised concerns about the fairness of black-box recommendation algorithmsdeployed on these marketplaces. Many complaints are centered aroundmarketplaces biasing the algorithms to preferentially favor their own `privatelabel' products over competitors. These concerns are exacerbated asmarketplaces increasingly de-emphasize or replace `organic' recommendationswith ad-driven `sponsored' recommendations, which include their own privatelabels. While these concerns have been covered in popular press and havespawned regulatory investigations, to our knowledge, there has not been anypublic audit of these marketplace algorithms. In this study, we bridge this gapby performing an end-to-end systematic audit of related item recommendations onAmazon. We propose a network-centric framework to quantify and compare thebiases across organic and sponsored related item recommendations. Along anumber of our proposed bias measures, we find that the sponsoredrecommendations are significantly more biased toward Amazon private labelproducts compared to organic recommendations. While our findings are primarilyinteresting to producers and sellers on Amazon, our proposed bias measures aregenerally useful for measuring link formation bias in any social or contentnetworks.

When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces