Adaptive, Personalized Diversity for Visual Discovery
This work addresses the challenge of enhancing user engagement in visual discovery for e-commerce platforms, though it appears incremental as it builds on existing browsing systems with personalization and diversification components.
The paper tackled the problem of improving visual browsing for e-commerce by developing a system that adapts to user interactions and presents diverse items, resulting in a strong lift in click-through-rate and session duration when tested on live traffic.
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.