Algorithmic Recommendation and Aesthetic Flourishing

Abstract In the age of streaming, we face a pressing problem of aesthetic choice: how are we to navigate the overwhelming quantity of content to which we now have access? Streaming platforms like Spotify and Netflix apply sophisticated machine learning tools to recommend personalized content to individual users. These recommender systems are presented to users as a technological solution to the problem of aesthetic choice, promising to help us discover new opportunities for engagement with aesthetic value. However, overreliance on algorithmic recommendation can lead to diminished aesthetic autonomy, reduced aesthetic exploration, and an overall reduction in the satisfaction we take in aesthetic engagement. This is largely due to the fact that, in their most popular implementations, recommender systems are optimized to captivate users. They aim to maximize our engagement by recommending both content and modes of engagement that are initially attractive and frictionless, but which ultimately reshape our aesthetic personalities in ways that anchor us to streaming platforms. This paper explores these concerns before concluding with an assessment of whether it is possible to use recommender systems in ways more conducive to our aesthetic flourishing.