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.

翻译：推荐系统在众多在线平台中扮演着关键角色，其核心目标是满足用户需求并提升用户留存率。由于直接优化用户留存具有挑战性，通常采用多种评估指标。现有方法通常将这些评估指标的优化建模为多任务学习问题，但往往忽略了用户对不同任务的需求具有个性化特征且会随时间动态演变。识别并追踪用户需求的演化过程有助于提升用户留存率。针对这一问题，我们引入"用户生命周期"概念，该周期包含多个阶段，每个阶段由用户对不同任务的差异化需求所表征。我们提出一种新颖的阶段自适应网络（STAN）框架用于建模用户生命周期阶段。STAN首先基于学习到的用户需求识别潜在的用户生命周期阶段，继而利用阶段表示增强多任务学习性能。在公开数据集和工业数据集上的实验结果表明，与现有最优方法相比，所提模型显著提升了多任务预测性能，凸显了在推荐系统中考虑用户生命周期阶段的重要性。此外，在线A/B测试显示，我们的模型优于现有模型，在单用户停留时间上实现3.05%的显著提升，转化率提升0.88%。这些结果表明，该方法有效提升了多任务推荐系统的整体效率。