Click-Through Rate (CTR) prediction holds a pivotal place in online advertising and recommender systems since CTR prediction performance directly influences the overall satisfaction of the users and the revenue generated by companies. Even so, CTR prediction is still an active area of research since it involves accurately modelling the preferences of users based on sparse and high-dimensional features where the higher-order interactions of multiple features can lead to different outcomes. Most CTR prediction models have relied on a single fusion and interaction learning strategy. The few CTR prediction models that have utilized multiple interaction modelling strategies have treated each interaction to be self-contained. In this paper, we propose a novel model named STEC that reaps the benefits of multiple interaction learning approaches in a single unified architecture. Additionally, our model introduces residual connections from different orders of interactions which boosts the performance by allowing lower level interactions to directly affect the predictions. Through extensive experiments on four real-world datasets, we demonstrate that STEC outperforms existing state-of-the-art approaches for CTR prediction thanks to its greater expressive capabilities.

翻译：点击率（CTR）预测在在线广告和推荐系统中占据核心地位，因为CTR预测性能直接影响用户的整体满意度及企业产生的营收。尽管如此，CTR预测仍是一个活跃的研究领域，因为它涉及基于稀疏高维特征精确建模用户偏好，其中多个特征的高阶交互可能导致不同结果。现有大多数CTR预测模型依赖单一的融合与交互学习策略，少数采用多种交互建模策略的模型则将每次交互视为独立单元。本文提出一种名为STEC的新模型，在统一架构中融合多种交互学习方法的优势。此外，模型引入不同阶次交互间的残差连接，使低阶交互能直接影响预测结果，从而提升性能。通过在四个真实数据集上的广泛实验，我们证明STEC凭借其更强的表达能力，在CTR预测任务上优于现有的最先进方法。