As user behaviors become complicated on business platforms, online recommendations focus more on how to touch the core conversions, which are highly related to the interests of platforms. These core conversions are usually continuous targets, such as \textit{watch time}, \textit{revenue}, and so on, whose predictions can be enhanced by previous discrete conversion actions. Therefore, multi-task learning (MTL) can be adopted as the paradigm to learn these hybrid targets. However, existing works mainly emphasize investigating the sequential dependence among discrete conversion actions, which neglects the complexity of dependence between discrete conversions and the final continuous conversion. Moreover, simultaneously optimizing hybrid tasks with stronger task dependence will suffer from volatile issues where the core regression task might have a larger influence on other tasks. In this paper, we study the MTL problem with hybrid targets for the first time and propose the model named Hybrid Targets Learning Network (HTLNet) to explore task dependence and enhance optimization. Specifically, we introduce label embedding for each task to explicitly transfer the label information among these tasks, which can effectively explore logical task dependence. We also further design the gradient adjustment regime between the final regression task and other classification tasks to enhance the optimization. Extensive experiments on two offline public datasets and one real-world industrial dataset are conducted to validate the effectiveness of HTLNet. Moreover, online A/B tests on the financial recommender system also show our model has superior improvement.

翻译：随着商业平台上用户行为日益复杂，在线推荐系统愈发关注如何触及与平台利益高度相关的核心转化目标。这些核心转化通常为连续型目标，如观看时长、收入等，其预测可通过先前的离散转化动作得到增强。因此，多任务学习（MTL）可作为学习这些混合目标的范式。然而现有工作主要强调探究离散转化动作间的序列依赖关系，忽视了离散转化与最终连续转化之间依赖关系的复杂性。此外，同时优化具有更强任务依赖关系的混合任务时，将面临核心回归任务可能对其他任务产生更大影响的波动性问题。本文首次研究面向混合目标的MTL问题，提出名为混合目标学习网络（HTLNet）的模型以探索任务依赖关系并增强优化。具体而言，我们为每个任务引入标签嵌入以显式传递任务间的标签信息，从而有效探索逻辑层面的任务依赖关系；同时进一步设计最终回归任务与其他分类任务间的梯度调节机制以增强优化效果。通过两个离线公开数据集与一个真实工业数据集的广泛实验验证了HTLNet的有效性。此外，在金融推荐系统上的在线A/B测试也表明该模型具有显著改进效果。