Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 25 Sep 2023.
Builds on
6 of 24 references.
References · 6
- 2016Wide & Deep Learning for Recommender Systemscited by 747
- 2018Offline A/B testing for Recommender Systemscited by 96
- 2022Off-Policy Evaluation for Large Action Spaces via Embeddingscited by 54
- 2016Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendationcited by 30
- 2020Carousel Personalization in Music Streaming Apps with Contextual Banditscited by 26
- 2020Deep Bayesian Bandits: Exploring in Online Personalized Recommendationscited by 23
Built on by
Not cited yet.