  Multi-Task Learning (MTL) plays a crucial role in real-world advertising
applications such as recommender systems, aiming to achieve robust
representations while minimizing resource consumption. MTL endeavors to
simultaneously optimize multiple tasks to construct a unified model serving
diverse objectives. In online advertising systems, tasks like Click-Through
Rate (CTR) and Conversion Rate (CVR) are often treated as MTL problems
concurrently. However, it has been overlooked that a conversion ($y_{cvr}=1$)
necessitates a preceding click ($y_{ctr}=1$). In other words, while certain CTR
tasks are associated with corresponding conversions, others lack such
associations. Moreover, the likelihood of noise is significantly higher in CTR
tasks where conversions do not occur compared to those where they do, and
existing methods lack the ability to differentiate between these two scenarios.
In this study, exposure labels corresponding to conversions are regarded as
definitive indicators, and a novel task-specific loss is introduced by
calculating a \textbf{p}air\textbf{wise} \textbf{r}anking (PWiseR) loss between
model predictions, manifesting as pairwise ranking loss, to encourage the model
to rely more on them. To demonstrate the effect of the proposed loss function,
experiments were conducted on different MTL and Single-Task Learning (STL)
models using four distinct public MTL datasets, namely Alibaba FR, NL, US, and
CCP, along with a proprietary industrial dataset. The results indicate that our
proposed loss function outperforms the BCE loss function in most cases in terms
of the AUC metric.
