mlsanityData to 7 Oct 2026

E-commerce in Your Inbox: Product Recommendations at Scale

arXiv 1606.07154 ↗First posted 23 Jun 2016cs.AI cs.IR cs.SIcited by 28

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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.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 6; 2023: 3; 2024: 4; 2025: 3; 2026: 1.

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  1. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  2. 2024IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commercecited by 11
  3. 2024MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understandingcited by 10
  4. 2021Query2Prod2Vec Grounded Word Embeddings for eCommercecited by 8
  5. 2021Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interactioncited by 5
  6. 2021Theoretical Understandings of Product Embedding for E-commerce Machine Learningcited by 4
  7. 2021Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddingscited by 4
  8. 2021"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shopscited by 4
  9. 2022Data Augmentation Strategies for Improving Sequential Recommender Systemscited by 4
  10. 2021Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directionscited by 2
  11. 2022Two Is Better Than One: Dual Embeddings for Complementary Product Recommendationscited by 2
  12. 2021Data Science Methodologies: Current Challenges and Future Approachescited by 1
16 more
  1. 2021Learning Complex Users' Preferences for Recommender Systemscited by 1
  2. 2022Large-Scale Sequential Learning for Recommender and Engineering Systemscited by 1
  3. 2024Within-basket Recommendation via Neural Pattern Associatorcited by 1
  4. 2025Interact2Vec -- An efficient neural network-based model for simultaneously learning users and items embeddings in recommender systemscited by 1
  5. 2021Variational Inference for Category Recommendation in E-Commerce platformscited by 0
  6. 2021Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendationcited by 0
  7. 2022Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)cited by 0
  8. 2022Track2Vec: fairness music recommendation with a GPU-free customizable-driven frameworkcited by 0
  9. 2022CASPR: Customer Activity Sequence-based Prediction and Representationcited by 0
  10. 2023Job recommendations: benchmarking of collaborative filtering methods for classifiedscited by 0
  11. 2023ITEm: Unsupervised Image-Text Embedding Learning for eCommercecited by 0
  12. 2023Multi-criteria recommendation systems to foster online grocerycited by 0
  13. 2024Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRxcited by 0
  14. 2025Revisiting Graph Projections for Effective Complementary Product Recommendationcited by 0
  15. 2025Using item recommendations and LLMs in marketing email titlescited by 0
  16. 2026Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contextscited by 0