Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search. Most existing CTR Prediction models utilize explicit feature interactions to overcome the performance bottleneck of implicit feature interactions. Hence, deep CTR models based on parallel structures (e.g., DCN, FinalMLP, xDeepFM) have been proposed to obtain joint information from different semantic spaces. However, these parallel subcomponents lack effective supervisory signals, making it challenging to efficiently capture valuable multi-views feature interaction information in different semantic spaces. To address this issue, we propose a simple yet effective novel CTR model: Contrast-enhanced Through Network for CTR (CETN), so as to ensure the diversity and homogeneity of feature interaction information. Specifically, CETN employs product-based feature interactions and the augmentation (perturbation) concept from contrastive learning to segment different semantic spaces, each with distinct activation functions. This improves diversity in the feature interaction information captured by the model. Additionally, we introduce self-supervised signals and through connection within each semantic space to ensure the homogeneity of the captured feature interaction information. The experiments and research conducted on four real datasets demonstrate that our model consistently outperforms twenty baseline models in terms of AUC and Logloss.

翻译：点击率（CTR）预测是个性化信息检索（如工业推荐系统、在线广告和网络搜索）中的关键任务。现有大多数CTR预测模型利用显式特征交互来克服隐式特征交互的性能瓶颈。因此，基于并行结构的深度CTR模型（例如DCN、FinalMLP、xDeepFM）被提出以从不同语义空间获取联合信息。然而，这些并行子组件缺乏有效的监督信号，导致难以高效捕获不同语义空间中具有价值的多视角特征交互信息。为解决该问题，我们提出一种简单而有效的新型CTR模型——对比增强通量网络（CETN），以确保特征交互信息的多样性与同质性。具体而言，CETN采用基于乘积的特征交互与对比学习中的增强（扰动）概念来划分不同语义空间，每个空间配置不同的激活函数，从而提升模型捕获特征交互信息的多样性。同时，我们在每个语义空间内引入自监督信号与通量连接，以确保所捕获特征交互信息的同质性。在四个真实数据集上的实验与研究结果表明，我们的模型在AUC和Logloss指标上持续优于二十个基线模型。