  E-commerce platforms provide entrances for customers to enter mini-apps that
can meet their specific shopping requirements. Trigger items displayed on
entrance icons can attract more entering. However, conventional
Click-Through-Rate (CTR) prediction models, which ignore user instant interest
in trigger item, fail to be applied to the new recommendation scenario dubbed
Trigger-Induced Recommendation in Mini-Apps (TIRA). Moreover, due to the high
stickiness of customers to mini-apps, we argue that existing trigger-based
methods that over-emphasize the importance of trigger items, are undesired for
TIRA, since a large portion of customer entries are because of their routine
shopping habits instead of triggers. We identify that the key to TIRA is to
extract customers' personalized entering intention and weigh the impact of
triggers based on this intention. To achieve this goal, we convert CTR
prediction for TIRA into a separate estimation form, and present Deep
Intention-Aware Network (DIAN) with three key elements: 1) Intent Net that
estimates user's entering intention, i.e., whether he/she is affected by the
trigger or by the habits; 2) Trigger-Aware Net and 3) Trigger-Free Net that
estimate CTRs given user's intention is to the trigger-item and the mini-app
respectively. Following a joint learning way, DIAN can both accurately predict
user intention and dynamically balance the results of trigger-free and
trigger-based recommendations based on the estimated intention. Experiments
show that DIAN advances state-of-the-art performance in a large real-world
dataset, and brings a 9.39% lift of online Item Page View and 4.74% CTR for
Juhuasuan, a famous mini-app of Taobao.
