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

arXiv: https://arxiv.org/abs/2106.12700 · HTML: https://mlsanity.com/paper/2106.12700/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control](https://mlsanity.com/paper/1506.02632/) (2015)
- [Reinforcement Mechanism Design, with Applications to Dynamic Pricing in Sponsored Search Auctions](https://mlsanity.com/paper/1711.10279/) (2017)
- [Form 10-Q Itemization](https://mlsanity.com/paper/2104.11783/) (2021)
- [High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model](https://mlsanity.com/paper/1804.08472/) (2018)
- [Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model](https://mlsanity.com/paper/2011.04171/) (2020)
- [A News-based Machine Learning Model for Adaptive Asset Pricing](https://mlsanity.com/paper/2106.07103/) (2021)
- [A Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning](https://mlsanity.com/paper/2103.14606/) (2021)

## Built on by (most cited first, 2 of 2)

- [The Adaptive Multi-Factor Model and the Financial Market](https://mlsanity.com/paper/2107.14410/) (2021)
- [Finding Lookalike Customers for E-Commerce Marketing](https://mlsanity.com/paper/2301.03147/) (2023)

## Abstract (arXiv)

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.