  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.
