Recommender Systems, Autonomy and User Engagement

Recommender systems form the backbone of modern e-commerce, suggesting items to users based on the collection of algorithmic data of a user's preferences. Companies that use recommender systems claim that they can give users what they want, or more precisely, what they desire. Netflix, for example, gives users recommended movies based on the user's behaviour on the platform, thereby listing new movies that the user may want to watch. This article explores whether there is a difference between what engages us, on the one hand, and what we truly want to want, on the other. This builds on the hierarchical structure of desires, as posed by Harry Frankfurt and Gerald Dworkin. Recommender systems, to use Frankfurt's terminology, may not allow for the formation of second-order desires, or for users to consider what they want to want. Indeed, recommender systems may rely on a narrow form of human engagement, a voyeuristic mode, rather than an active wanting. In bypassing second-order desires, there is a risk that recommender systems can start to control the user, rather than the user controlling the algorithm. This raises important questions concerning human autonomy, trustworthiness, and Byung-Chul Han's conception of an information regime, where the owners of the data make decisions about what users consume online, and ultimately, how they live their lives.