  We describe a completely automated large scale visual recommendation system
for fashion. Existing approaches have primarily relied on purely computational
models to solving this problem that ignore the role of users in the system. In
this paper, we propose to overcome this limitation by incorporating a
user-centric design of visual fashion recommendations. Specifically, we propose
a technique that augments 'user preferences' in models by exploiting elasticity
in fashion choices. We further design a user study on these choices and gather
results from the 'wisdom of crowd' for deeper analysis. Our key insights learnt
through these results suggest that fashion preferences when constrained to a
particular class, contain important behavioral signals that are often ignored
in recommendation design. Further, presence of such classes also reflect strong
correlations to visual perception which can be utilized to provide
aesthetically pleasing user experiences. Finally, we illustrate that user
approval of visual fashion recommendations can be substantially improved by
carefully incorporating these user-centric feedback into the system framework.
