Heterogeneity in Customization of Recommender Systems By Users with Homogenous Preferences

Recommender systems must find items that match the heterogeneous preferences of its users. Customizable recommenders allow users to directly manipulate the system's algorithm in order to help it match those preferences. However, customizing may demand a certain degree of skill and new users particularly may struggle to effectively customize the system. In user studies of two different systems, I show that there is considerable heterogeneity in the way that new users will try to customize a recommender, even within groups of users with similar underlying preferences. Furthermore, I show that this heterogeneity persists beyond the first few interactions with the recommender. System designs should consider this heterogeneity so that new users can both receive good recommendations in their early interactions as well as learn how to effectively customize the system for their preferences.