Page-level Optimization of e-Commerce Item Recommendations

TL;DR

This paper introduces a scalable deep learning system for real-time personalized optimization of item recommendation modules on e-commerce product pages, significantly improving user engagement and sales metrics.

Contribution

The authors develop an end-to-end production system that dynamically personalizes recommendation module selection and ordering using deep neural networks, outperforming static configurations.

Findings

2.48% increase in click-through rate

7.34% increase in purchase-through rate

Significant improvements over static recommendation methods

Abstract

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.