  User beliefs about algorithmic systems are constantly co-produced through
user interaction and the complex socio-technical systems that generate
recommendations. Identifying these beliefs is crucial because they influence
how users interact with recommendation algorithms. With no prior work on user
beliefs of algorithmic video recommendations, practitioners lack relevant
knowledge to improve the user experience of such systems. To address this
problem, we conducted semi-structured interviews with middle-aged YouTube video
consumers to analyze their user beliefs about the video recommendation system.
Our analysis revealed different factors that users believe influence their
recommendations. Based on these factors, we identified four groups of user
beliefs: Previous Actions, Social Media, Recommender System, and Company
Policy. Additionally, we propose a framework to distinguish the four main
actors that users believe influence their video recommendations: the current
user, other users, the algorithm, and the organization. This framework provides
a new lens to explore design suggestions based on the agency of these four
actors. It also exposes a novel aspect previously unexplored: the effect of
corporate decisions on the interaction with algorithmic recommendations. While
we found that users are aware of the existence of the recommendation system on
YouTube, we show that their understanding of this system is limited.
