Ads relevance models are crucial in determining the relevance between user search queries and ad offers, often framed as a classification problem. The complexity of modeling increases significantly with multiple ad types and varying scenarios that exhibit both similarities and differences. In this work, we introduce a novel multi-faceted attention model that performs task aware feature combination and cross task interaction modeling. Our technique formulates the feature combination problem as "language" modeling with auto-regressive attentions across both feature and task dimensions. Specifically, we introduce a new dimension of task ID encoding for task representations, thereby enabling precise relevance modeling across diverse ad scenarios with substantial improvement in generality capability for unseen tasks. We demonstrate that our model not only effectively handles the increased computational and maintenance demands as scenarios proliferate, but also outperforms generalized DNN models and even task-specific models across a spectrum of ad applications using a single unified model.

翻译：广告相关性模型在判断用户搜索查询与广告投放之间的相关性方面至关重要，通常被构建为分类问题。随着广告类型和场景的多样化，且这些场景既存在相似性又存在差异，建模的复杂性显著增加。本研究提出了一种新颖的多维度注意力模型，该模型能够执行任务感知的特征组合与跨任务交互建模。我们的技术将特征组合问题构建为一种“语言”建模，通过跨特征维度和任务维度的自回归注意力机制实现。具体而言，我们引入了任务ID编码这一新维度来表征任务，从而能够对不同广告场景进行精确的相关性建模，并显著提升模型对未见任务的泛化能力。实验表明，我们的模型不仅能够有效应对场景激增带来的计算和维护需求，而且通过单一统一模型，在一系列广告应用中超越了广义深度神经网络模型，甚至优于任务专用模型。