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.