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Recommendation systems play a vital role in many online platforms, with theirprimary objective being to satisfy and retain users. As directly optimizinguser retention is challenging, multiple evaluation metrics are often employed.Existing methods generally formulate the optimization of these evaluationmetrics as a multitask learning problem, but often overlook the fact that userpreferences for different tasks are personalized and change over time.Identifying and tracking the evolution of user preferences can lead to betteruser retention. To address this issue, we introduce the concept of "userlifecycle", consisting of multiple stages characterized by users' varyingpreferences for different tasks. We propose a novel Stage-Adaptive Network(STAN) framework for modeling user lifecycle stages. STAN first identifieslatent user lifecycle stages based on learned user preferences, and thenemploys the stage representation to enhance multi-task learning performance.Our experimental results using both public and industrial datasets demonstratethat the proposed model significantly improves multi-task predictionperformance compared to state-of-the-art methods, highlighting the importanceof 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% inCVR. These results indicate that our approach effectively improves the overallefficiency of the multi-task recommendation system.

STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation