This is Northeastern University’s personalization research page for our price discrimination project.
0px; “> Today, many e-commerce websites personalize their content,
including Netflix (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
differences 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 find evidence for some form of personalization on nine of these e-commerce sites. Third, we
investigate the effect of user behaviors on personalization.
We create fake accounts to simulate different user features
including web browser/OS choice, owning an account, and
history of purchased or viewed products. Overall, we find
numerous instances of price steering and discrimination on
a variety of top e-commerce sites.
Source: Research | Price Discrimination