Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model Approach
This work addresses the challenge of optimizing push notification effectiveness for e-commerce platforms, though it appears incremental as it builds on existing recommendation techniques.
The paper tackled the problem of selecting complementary products for e-commerce push notifications to maximize open rates, proposing a mixture model that learns latent contexts from user and item profiles. Experimental results on a live mobile app showed the method significantly outperformed existing solutions.
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.