  Recommendation systems play a vital role in many online platforms, with their
primary objective being to satisfy and retain users. As directly optimizing
user retention is challenging, multiple evaluation metrics are often employed.
Existing methods generally formulate the optimization of these evaluation
metrics as a multitask learning problem, but often overlook the fact that user
preferences for different tasks are personalized and change over time.
Identifying and tracking the evolution of user preferences can lead to better
user retention. To address this issue, we introduce the concept of "user
lifecycle", consisting of multiple stages characterized by users' varying
preferences for different tasks. We propose a novel Stage-Adaptive Network
(STAN) framework for modeling user lifecycle stages. STAN first identifies
latent user lifecycle stages based on learned user preferences, and then
employs the stage representation to enhance multi-task learning performance.
Our experimental results using both public and industrial datasets demonstrate
that the proposed model significantly improves multi-task prediction
performance compared to state-of-the-art methods, highlighting the importance
of considering user lifecycle stages in recommendation systems. Furthermore,
online A/B testing reveals that our model outperforms the existing model,
achieving a significant improvement of 3.05% in staytime per user and 0.88% in
CVR. These results indicate that our approach effectively improves the overall
efficiency of the multi-task recommendation system.
