  Recommender systems can be a helpful tool for recommending content but they
can also influence users' preferences. One sociological theory for this
influence is that companies are incentivised to influence preferences to make
users easier to predict and thus more profitable by making it harder to change
preferences. This paper seeks to test that theory empirically. We use
\textit{Barrier-to-Exit}, a metric for how difficult it is for users to change
preferences, to analyse a large dataset of Amazon Book Ratings from 1998 to
2018. We focus the analysis on users who have changed preferences according to
Barrier-to-Exit. To assess the growth of Barrier-to-Exit over time, we
developed a linear mixed-effects model with crossed random effects for users
and categories. Our findings indicate a highly significant growth of
Barrier-to-Exit over time, suggesting that it has become more difficult for the
analysed subset of users to change their preferences. However, it should be
noted that these findings come with several statistical and methodological
caveats including sample bias and construct validity issues related to
Barrier-to-Exit. We discuss the strengths and limitations of our approach and
its implications. Additionally, we highlight the challenges of creating
context-sensitive and generalisable measures for complex socio-technical
concepts such as "difficulty to change preferences." We conclude with a call
for further research: to curb the potential threats of preference manipulation,
we need more measures that allow us to compare commercial as well as
non-commercial systems.
