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.

移动电子商务平台中，提供精准的商品推荐至关重要。通过将可视控件放置在界面顶部，以便在有限的屏幕空间内显示给用户，可以有效吸引用户的注意力和提高用户投射率。 这项工作提出了一个基于上下文多臂赌博问题和多样性层次相结合的两阶段排序框架，用于对垂直可视控件进行个性化排序，通过在印度主要时尚电子商务平台 Myntra 上进行离线和在线 A/B 测试，证明了该框架的有效性。