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:多重设想的排名旨在利用来自多个领域或渠道的数据,培训统一的排名模型,以改进每个设想方案的业绩。虽然关于这项任务的研究取得了重要进展,但仍然没有考虑跨设想关系,从而导致学习能力的限制和相互建模方面的困难。在本文件中,我们提议对多重设想的排名采取混合相互约束办法(HC2),以加强数据建模的相互关系,我们精心设计一种混合对比学习方法,以捕捉多种设想方案之间的共性和差异。我们的方法核心包括两种精心拟订的对比性损失,即普遍和个别对比性损失,分别旨在获取共同知识和具体设想方案的知识。为适应复杂的多设想环境,我们提出了一系列重要的改进。关于普遍对比性损失,我们通过扩大对比性样本(标签和扩散噪音增加对比性样本)和重新加权对比性样本(对相似性加权)。关于个人对比性损失,我们采用了分别旨在获取共同知识和具体情景知识的对比性损失、对比性损失,我们采用了以大幅对比性测试为基准的在线测试战略。我们用基于实际的升级和跨级的测试模型,分别进行有意义的升级和测试。