We present a large-scale study of television viewing habits, focusing on how individuals adapt their preferences when consuming content with others. While there has been a great deal of research on modeling individual preferences, there has been considerably less work studying the preferences of groups, due mostly to the difficulty of collecting group data. In contrast to most past work that has relied either on small-scale surveys, prototypes, or a relatively limited amount of group preference data, we explore more than 4 million logged household views paired with individual-level demographic and co-viewing information. Our analysis reveals how engagement in group viewing varies by viewer and content type, and how viewing patterns shift across various group contexts. Furthermore, we leverage this large-scale dataset to directly estimate how individual preferences are combined in group settings, finding subtle deviations from traditional models of preference aggregation. We present a simple model which captures these effects and discuss the impact of these findings on the design of group recommendation systems.
['Allison June-Barlow Chaney', 'Mike Gartrell', 'Jake M. Hofman', 'John Guiver', 'Noam Koenigstein', 'Pushmeet Kohli', 'Ulrich Paquet']
A large-scale exploration of group viewing patterns