# Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model Approach

arXiv: https://arxiv.org/abs/1707.08113 · HTML: https://mlsanity.com/paper/1707.08113/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems](https://mlsanity.com/paper/2404.00903/) (2024)
- [TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences](https://mlsanity.com/paper/2409.12972/) (2024)
- [Information Discovery in e-Commerce](https://mlsanity.com/paper/2410.05763/) (2024)
- [Complementary Recommendation in E-commerce: Definition, Approaches, and Future Directions](https://mlsanity.com/paper/2403.16135/) (2024)
- [A Self-Triggered Agentic Push Recommendation System](https://mlsanity.com/paper/2608.01949/) (2026)
- [Beyond Co-purchase Relation: Evolution of Complementary Recommendations at Allegro](https://mlsanity.com/paper/2609.05063/) (2026)

## Abstract (arXiv)

Push notification is a key component for E-commerce mobile applications, which has been extensively used for user growth and engagement. The effectiveness of the push notification is generally measured by message open rate. A push message can contain a recommended product, a shopping news and etc., but often only one or two items can be shown in the push message due to the limit of display space. This paper proposes a mixture model approach for predicting push message open rate for a post-purchase complementary product recommendation task. The mixture model is trained to learn latent prediction contexts, which are determined by user and item profiles, and then make open rate predictions accordingly. The item with the highest predicted open rate is then chosen to be included in the push notification message for each user. The parameters of the mixture model are optimized using an EM algorithm. A set of experiments are conducted to evaluate the proposed method live with a popular E-Commerce mobile app. The results show that the proposed method is superior than several existing solutions by a significant margin.