Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic
This work addresses the need for e-commerce companies to expand their exposure on organic search, though it appears incremental as it applies existing methods to a specific domain.
The paper tackled the problem of increasing e-commerce organic search traffic by creating millions of new landing pages using a transformer language model, resulting in improved prominence and clicks on the channel.
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.