An Efficient Group-based Search Engine Marketing System for E-Commerce

TL;DR

This paper presents a scalable, efficient group-based bidding system for e-commerce search engine marketing that leverages language signals and clustering to handle massive bid volumes with high sparsity.

Contribution

The paper introduces an end-to-end, production-efficient bidding system utilizing Transformer-based representations and clustering to reduce computation and improve performance in large-scale e-commerce.

Findings

Handles tens of millions of bids daily

Achieves significant reduction in computation load

Demonstrates improved online and offline performance

Abstract

With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies. The critical challenges faced by a modern industrial-level bidding system include: 1. the catalog is enormous, and the relevant bidding features are of high sparsity; 2. the large volume of bidding requests induces significant computation burden to both the offline and online serving. Leveraging extraneous user-item information proves essential to mitigate the sparsity issue, for which we exploit the natural language signals from the users' query and the contextual knowledge from the products. In particular, we extract the vector representations of ads via the Transformer model and leverage their geometric relation to building collaborative bidding predictions via clustering. The two-step procedure also significantly reduces the computation stress of bid evaluation and optimization. In this paper, we introduce the end-to-end structure of the bidding system for search engine marketing for Walmart e-commerce, which successfully handles tens of millions of bids each day. We analyze the online and offline performances of our approach and discuss how we find it as a production-efficient solution.