Gretarsson, BrynjarO'Donovan, JohnBostandjiev, SvetlinHall, ChristopherHöllerer, TobiasG. Melancon, T. Munzner, and D. Weiskopf2014-02-212014-02-2120101467-8659https://doi.org/10.1111/j.1467-8659.2009.01679.xWe present SmallWorlds, a visual interactive graph-based interface that allows users to specify, refine and build item-preference profiles in a variety of domains. The interface facilitates expressions of taste through simple graph interactions and these preferences are used to compute personalized, fully transparent item recommendations for a target user. Predictions are based on a collaborative analysis of preference data from a user s direct peer group on a social network. We find that in addition to receiving transparent and accurate item recommendations, users also learn a wealth of information about the preferences of their peers through interaction with our visualization. Such information is not easily discoverable in traditional text based interfaces. A detailed analysis of our design choices for visual layout, interaction and prediction techniques is presented. Our evaluations discuss results from a user study in which SmallWorlds was deployed as an interactive recommender system on Facebook.