A Real-Time Whole Page Personalization Framework for E-Commerce
Aditya Mantha, Anirudha Sundaresan, Shashank Kedia, Yokila Arora,, Shubham Gupta, Gaoyang Wang, Praveenkumar Kanumala, Stephen Guo, Kannan Achan

TL;DR
This paper introduces a scalable real-time personalization system for e-commerce homepages that dynamically ranks carousels to improve user engagement and business metrics, demonstrated through Walmart's online grocery platform.
Contribution
The work presents a novel, flexible model and system architecture for real-time ranking of page components, optimized for low latency and extendable to various settings.
Findings
Increased user engagement and item discovery.
Significant lift in add-to-carts per visitor.
Effective online evaluation benchmarks.
Abstract
E-commerce platforms consistently aim to provide personalized recommendations to drive user engagement, enhance overall user experience, and improve business metrics. Most e-commerce platforms contain multiple carousels on their homepage, each attempting to capture different facets of the shopping experience. Given varied user preferences, optimizing the placement of these carousels is critical for improved user satisfaction. Furthermore, items within a carousel may change dynamically based on sequential user actions, thus necessitating online ranking of carousels. In this work, we present a scalable end-to-end production system to optimally rank item-carousels in real-time on the Walmart online grocery homepage. The proposed system utilizes a novel model that captures the user's affinity for different carousels and their likelihood to interact with previously unseen items. Our system…
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