Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic

TL;DR

This paper introduces a transformer-based system for creating and managing millions of landing pages to enhance e-commerce organic search traffic, demonstrating real-world effectiveness and optimality.

Contribution

It presents a scalable, transformer-based approach for automated landing page creation tailored to improve organic search visibility in e-commerce.

Findings

Successfully manages millions of landing pages in production.

Demonstrates improved organic search traffic performance.

Identifies state-of-the-art language models as optimal solutions.

Abstract

Organic search comprises a large portion of the total traffic for e-commerce companies. One approach to expand company's exposure on organic search channel lies on creating landing pages having broader coverage on customer intentions. In this paper, we present a transformer language model based organic channel page management system aiming at increasing prominence of the company's overall clicks on the channel. Our system successfully handles the creation and deployment process of millions of new landing pages. We show and discuss the real-world performances of state-of-the-art language representation learning method, and reveal how we find them as the production-optimal solutions.