  Algorithmic recommendations mediate interactions between millions of
customers and products (in turn, their producers and sellers) on large
e-commerce marketplaces like Amazon. In recent years, the producers and sellers
have raised concerns about the fairness of black-box recommendation algorithms
deployed on these marketplaces. Many complaints are centered around
marketplaces biasing the algorithms to preferentially favor their own `private
label' products over competitors. These concerns are exacerbated as
marketplaces increasingly de-emphasize or replace `organic' recommendations
with ad-driven `sponsored' recommendations, which include their own private
labels. While these concerns have been covered in popular press and have
spawned regulatory investigations, to our knowledge, there has not been any
public audit of these marketplace algorithms. In this study, we bridge this gap
by performing an end-to-end systematic audit of related item recommendations on
Amazon. We propose a network-centric framework to quantify and compare the
biases across organic and sponsored related item recommendations. Along a
number of our proposed bias measures, we find that the sponsored
recommendations are significantly more biased toward Amazon private label
products compared to organic recommendations. While our findings are primarily
interesting to producers and sellers on Amazon, our proposed bias measures are
generally useful for measuring link formation bias in any social or content
networks.
