  Search queries are appropriate when users have explicit intent, but they
perform poorly when the intent is difficult to express or if the user is simply
looking to be inspired. Visual browsing systems allow e-commerce platforms to
address these scenarios while offering the user an engaging shopping
experience. Here we explore extensions in the direction of adaptive
personalization and item diversification within Stream, a new form of visual
browsing and discovery by Amazon. Our system presents the user with a diverse
set of interesting items while adapting to user interactions. Our solution
consists of three components (1) a Bayesian regression model for scoring the
relevance of items while leveraging uncertainty, (2) a submodular
diversification framework that re-ranks the top scoring items based on
category, and (3) personalized category preferences learned from the user's
behavior. When tested on live traffic, our algorithms show a strong lift in
click-through-rate and session duration.
