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

TL;DR

This paper introduces a mixture model approach to predict and select the most engaging complementary products for push notifications in e-commerce, improving open rates significantly.

Contribution

It presents a novel mixture model trained with EM algorithm to predict push notification open rates based on user and item profiles, optimizing product recommendations.

Findings

Outperforms existing methods in open rate prediction

Effective in live e-commerce app deployment

Significant improvement in user engagement metrics

Abstract

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.