Measuring Price Discrimination and Steering on E-commerce Web Sites
Today, many e-commerce websites personalize their content,
including Net
ix (movie recommendations), Amazon (product
suggestions), and Yelp (business reviews). In many
cases, personalization provides advantages for users: for example,
when a user searches for an ambiguous query such as
\router," Amazon may be able to suggest the woodworking
tool instead of the networking device. However, personalization
on e-commerce sites may also be used to the user's disadvantage
by manipulating the products shown (price steering)
or by customizing the prices of products (price discrimination).
Unfortunately, today, we lack the tools and techniques
necessary to be able to detect such behavior.
In this paper, we make three contributions towards addressing
this problem. First, we develop a methodology for
accurately measuring when price steering and discrimination
occur and implement it for a variety of e-commerce web
sites. While it may seem conceptually simple to detect differences
between users' results, accurately attributing these
dierences to price discrimination and steering requires correctly
addressing a number of sources of noise. Second, we
use the accounts and cookies of over 300 real-world users
to detect price steering and discrimination on 16 popular
e-commerce sites. We nd evidence for some form of personalization
on nine of these e-commerce sites. Third, we
investigate the eect of user behaviors on personalization.
We create fake accounts to simulate dierent user features
including web browser/OS choice, owning an account, and
history of purchased or viewed products. Overall, we nd
numerous instances of price steering and discrimination on
a variety of top e-commerce sites.