# E-commerce in Your Inbox: Product Recommendations at Scale

arXiv: https://arxiv.org/abs/1606.07154 · HTML: https://mlsanity.com/paper/1606.07154/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2016.

## Built on by (most cited first, 28 of 28)

- [Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification](https://mlsanity.com/paper/2101.07361/) (2021)
- [IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce](https://mlsanity.com/paper/2406.10173/) (2024)
- [MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding](https://mlsanity.com/paper/2406.10701/) (2024)
- [Query2Prod2Vec Grounded Word Embeddings for eCommerce](https://mlsanity.com/paper/2104.02061/) (2021)
- [Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction](https://mlsanity.com/paper/2104.08874/) (2021)
- [Theoretical Understandings of Product Embedding for E-commerce Machine Learning](https://mlsanity.com/paper/2102.12029/) (2021)
- [Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings](https://mlsanity.com/paper/2105.08585/) (2021)
- ["Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops](https://mlsanity.com/paper/2107.03256/) (2021)
- [Data Augmentation Strategies for Improving Sequential Recommender Systems](https://mlsanity.com/paper/2203.14037/) (2022)
- [Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions](https://mlsanity.com/paper/2112.11022/) (2021)
- [Two Is Better Than One: Dual Embeddings for Complementary Product Recommendations](https://mlsanity.com/paper/2211.14982/) (2022)
- [Data Science Methodologies: Current Challenges and Future Approaches](https://mlsanity.com/paper/2106.07287/) (2021)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)
- [Large-Scale Sequential Learning for Recommender and Engineering Systems](https://mlsanity.com/paper/2205.06893/) (2022)
- [Within-basket Recommendation via Neural Pattern Associator](https://mlsanity.com/paper/2401.16433/) (2024)
- [Interact2Vec -- An efficient neural network-based model for simultaneously learning users and items embeddings in recommender systems](https://mlsanity.com/paper/2506.22648/) (2025)
- [Variational Inference for Category Recommendation in E-Commerce platforms](https://mlsanity.com/paper/2104.07748/) (2021)
- [Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation](https://mlsanity.com/paper/2108.13824/) (2021)
- [Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)](https://mlsanity.com/paper/2202.13240/) (2022)
- [Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework](https://mlsanity.com/paper/2210.16590/) (2022)
- [CASPR: Customer Activity Sequence-based Prediction and Representation](https://mlsanity.com/paper/2211.09174/) (2022)
- [Job recommendations: benchmarking of collaborative filtering methods for classifieds](https://mlsanity.com/paper/2301.07946/) (2023)
- [ITEm: Unsupervised Image-Text Embedding Learning for eCommerce](https://mlsanity.com/paper/2311.02084/) (2023)
- [Multi-criteria recommendation systems to foster online grocery](https://mlsanity.com/paper/2312.08393/) (2023)
- [Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx](https://mlsanity.com/paper/2408.08024/) (2024)
- [Revisiting Graph Projections for Effective Complementary Product Recommendation](https://mlsanity.com/paper/2506.09209/) (2025)
- [Using item recommendations and LLMs in marketing email titles](https://mlsanity.com/paper/2508.20024/) (2025)
- [Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts](https://mlsanity.com/paper/2604.15581/) (2026)

## Abstract (arXiv)

In recent years online advertising has become increasingly ubiquitous and effective. Advertisements shown to visitors fund sites and apps that publish digital content, manage social networks, and operate e-mail services. Given such large variety of internet resources, determining an appropriate type of advertising for a given platform has become critical to financial success. Native advertisements, namely ads that are similar in look and feel to content, have had great success in news and social feeds. However, to date there has not been a winning formula for ads in e-mail clients. In this paper we describe a system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users. We propose to use a novel neural language-based algorithm specifically tailored for delivering effective product recommendations, which was evaluated against baselines that included showing popular products and products predicted based on co-occurrence. We conducted rigorous offline testing using a large-scale product purchase data set, covering purchases of more than 29 million users from 172 e-commerce websites. Ads in the form of product recommendations were successfully tested on online traffic, where we observed a steady 9% lift in click-through rates over other ad formats in mail, as well as comparable lift in conversion rates. Following successful tests, the system was launched into production during the holiday season of 2014.