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.

翻译：多场景广告排序旨在利用来自多个域或渠道的数据训练统一排序模型，以提升每个独立场景的性能。尽管该任务的研究已取得重要进展，但仍缺乏对跨场景关系的考量，导致学习能力受限且相互关系建模困难。本文提出一种混合对比约束方法（HC²）用于多场景广告排序。为增强数据相互关系的建模能力，我们精心设计了混合对比学习方法以捕获多场景间的共性与差异。该方法的核心包含两种精心设计的对比损失函数——广义对比损失与个体对比损失，分别旨在捕获通用知识与场景特定知识。为使对比学习适配复杂多场景设定，我们提出了一系列重要改进。对于广义对比损失，我们通过扩展对比样本（标签感知与扩散噪声增强的对比样本）和重加权对比样本（互惠相似度加权）来增强对比学习。对于个体对比损失，分别采用基于dropout的增广策略与跨场景编码方法生成有意义的正负对比样本。通过离线评估与在线测试的大量实验，并与多个强基线方法对比，验证了所提HC²方法的有效性。