  Recommender systems rely heavily on the predictive accuracy of the learning
algorithm. Most work on improving accuracy has focused on the learning
algorithm itself. We argue that this algorithmic focus is myopic. In
particular, since learning algorithms generally improve with more and better
data, we propose shaping the feedback generation process as an alternate and
complementary route to improving accuracy. To this effect, we explore how
changes to the user interface can impact the quality and quantity of feedback
data -- and therefore the learning accuracy. Motivated by information foraging
theory, we study how feedback quality and quantity are influenced by interface
design choices along two axes: information scent and information access cost.
We present a user study of these interface factors for the common task of
picking a movie to watch, showing that these factors can effectively shape and
improve the implicit feedback data that is generated while maintaining the user
experience.
