  The item details page (IDP) is a web page on an e-commerce website that
provides information on a specific product or item listing. Just below the
details of the item on this page, the buyer can usually find recommendations
for other relevant items. These are typically in the form of a series of
modules or carousels, with each module containing a set of recommended items.
The selection and ordering of these item recommendation modules are intended to
increase discover-ability of relevant items and encourage greater user
engagement, while simultaneously showcasing diversity of inventory and
satisfying other business objectives. Item recommendation modules on the IDP
are often curated and statically configured for all customers, ignoring
opportunities for personalization. In this paper, we present a scalable
end-to-end production system to optimize the personalized selection and
ordering of item recommendation modules on the IDP in real-time by utilizing
deep neural networks. Through extensive offline experimentation and online A/B
testing, we show that our proposed system achieves significantly higher
click-through and conversion rates compared to other existing methods. In our
online A/B test, our framework improved click-through rate by 2.48% and
purchase-through rate by 7.34% over a static configuration.
