Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience
This addresses the problem of maximizing user engagement on mobile e-commerce platforms by improving widget ranking, though it appears incremental as it builds on existing bandit and diversity methods.
The paper tackles the challenge of optimizing widget placement on mobile e-commerce homepages with limited screen space by modeling it as a contextual multi-arm bandit problem with delayed batch feedback, and demonstrates effectiveness through offline and online A/B testing on Myntra's platform.
In the realm of e-commerce, popular platforms utilize widgets to recommend advertisements and products to their users. However, the prevalence of mobile device usage on these platforms introduces a unique challenge due to the limited screen real estate available. Consequently, the positioning of relevant widgets becomes pivotal in capturing and maintaining customer engagement. Given the restricted screen size of mobile devices, widgets placed at the top of the interface are more prominently displayed and thus attract greater user attention. Conversely, widgets positioned further down the page require users to scroll, resulting in reduced visibility and subsequent lower impression rates. Therefore it becomes imperative to place relevant widgets on top. However, selecting relevant widgets to display is a challenging task as the widgets can be heterogeneous, widgets can be introduced or removed at any given time from the platform. In this work, we model the vertical widget reordering as a contextual multi-arm bandit problem with delayed batch feedback. The objective is to rank the vertical widgets in a personalized manner. We present a two-stage ranking framework that combines contextual bandits with a diversity layer to improve the overall ranking. We demonstrate its effectiveness through offline and online A/B results, conducted on proprietary data from Myntra, a major fashion e-commerce platform in India.