  Multi-scenario ad ranking aims at leveraging the data from multiple domains
or channels for training a unified ranking model to improve the performance at
each individual scenario. Although the research on this task has made important
progress, it still lacks the consideration of cross-scenario relations, thus
leading to limitation in learning capability and difficulty in interrelation
modeling. In this paper, we propose a Hybrid Contrastive Constrained approach
(HC^2) for multi-scenario ad ranking. To enhance the modeling of data
interrelation, we elaborately design a hybrid contrastive learning approach to
capture commonalities and differences among multiple scenarios. The core of our
approach consists of two elaborated contrastive losses, namely generalized and
individual contrastive loss, which aim at capturing common knowledge and
scenario-specific knowledge, respectively. To adapt contrastive learning to the
complex multi-scenario setting, we propose a series of important improvements.
For generalized contrastive loss, we enhance contrastive learning by extending
the contrastive samples (label-aware and diffusion noise enhanced contrastive
samples) and reweighting the contrastive samples (reciprocal similarity
weighting). For individual contrastive loss, we use the strategies of
dropout-based augmentation and {cross-scenario encoding} for generating
meaningful positive and negative contrastive samples, respectively. Extensive
experiments on both offline evaluation and online test have demonstrated the
effectiveness of the proposed HC$^2$ by comparing it with a number of
competitive baselines.
