  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.
