# Finding Lookalike Customers for E-Commerce Marketing

arXiv: https://arxiv.org/abs/2301.03147 · HTML: https://mlsanity.com/paper/2301.03147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest](https://mlsanity.com/paper/2007.03634/) (2020)
- [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)
- [FrequentNet: A Novel Interpretable Deep Learning Model for Image Classification](https://mlsanity.com/paper/2001.01034/) (2020)
- [Clustering Structure of Microstructure Measures](https://mlsanity.com/paper/2107.02283/) (2021)
- [The Adaptive Multi-Factor Model and the Financial Market](https://mlsanity.com/paper/2107.14410/) (2021)
- [An Efficient Group-based Search Engine Marketing System for E-Commerce](https://mlsanity.com/paper/2106.12700/) (2021)
- [Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic](https://mlsanity.com/paper/2209.10792/) (2022)
- [Knowledge Base Completion using Web-Based Question Answering and Multimodal Fusion](https://mlsanity.com/paper/2211.07098/) (2022)
- [Query-Driven Knowledge Base Completion using Multimodal Path Fusion over Multimodal Knowledge Graph](https://mlsanity.com/paper/2212.01923/) (2022)

## Built on by (most cited first, 1 of 1)

- [UNICON: A unified framework for behavior-based consumer segmentation in e-commerce](https://mlsanity.com/paper/2309.13068/) (2023)

## Abstract (arXiv)

Customer-centric marketing campaigns generate a large portion of e-commerce website traffic for Walmart. As the scale of customer data grows larger, expanding the marketing audience to reach more customers is becoming more critical for e-commerce companies to drive business growth and bring more value to customers. In this paper, we present a scalable and efficient system to expand targeted audience of marketing campaigns, which can handle hundreds of millions of customers. We use a deep learning based embedding model to represent customers and an approximate nearest neighbor search method to quickly find lookalike customers of interest. The model can deal with various business interests by constructing interpretable and meaningful customer similarity metrics. We conduct extensive experiments to demonstrate the great performance of our system and customer embedding model.