  Modern mobile applications heavily rely on the notification system to acquire
daily active users and enhance user engagement. Being able to proactively reach
users, the system has to decide when to send notifications to users. Although
many researchers have studied optimizing the timing of sending notifications,
they only utilized users' contextual features, without modeling users' behavior
patterns. Additionally, these efforts only focus on individual notifications,
and there is a lack of studies on optimizing the holistic timing of multiple
notifications within a period. To bridge these gaps, we propose the Temporal
Interaction Model (TIM), which models users' behavior patterns by estimating
CTR in every time slot over a day in our short video application Kuaishou. TIM
leverages long-term user historical interaction sequence features such as
notification receipts, clicks, watch time and effective views, and employs a
temporal attention unit (TAU) to extract user behavior patterns. Moreover, we
provide an elegant strategy of holistic notifications send time control to
improve user engagement while minimizing disruption. We evaluate the
effectiveness of TIM through offline experiments and online A/B tests. The
results indicate that TIM is a reliable tool for forecasting user behavior,
leading to a remarkable enhancement in user engagement without causing undue
disturbance.
