Effective feature interaction modeling is critical for enhancing the accuracy of click-through rate (CTR) prediction in industrial recommender systems. Most of the current deep CTR models resort to building complex network architectures to better capture intricate feature interactions or user behaviors. However, we identify two limitations in these models: (1) the samples given to the model are undifferentiated, which may lead the model to learn a larger number of easy samples in a single-minded manner while ignoring a smaller number of hard samples, thus reducing the model's generalization ability; (2) differentiated feature interaction encoders are designed to capture different interactions information but receive consistent supervision signals, thereby limiting the effectiveness of the encoder. To bridge the identified gaps, this paper introduces a novel CTR prediction framework by integrating the plug-and-play Twin Focus (TF) Loss, Sample Selection Embedding Module (SSEM), and Dynamic Fusion Module (DFM), named the Twin Focus Framework for CTR (TF4CTR). Specifically, the framework employs the SSEM at the bottom of the model to differentiate between samples, thereby assigning a more suitable encoder for each sample. Meanwhile, the TF Loss provides tailored supervision signals to both simple and complex encoders. Moreover, the DFM dynamically fuses the feature interaction information captured by the encoders, resulting in more accurate predictions. Experiments on five real-world datasets confirm the effectiveness and compatibility of the framework, demonstrating its capacity to enhance various representative baselines in a model-agnostic manner. To facilitate reproducible research, our open-sourced code and detailed running logs will be made available at: https://github.com/salmon1802/TF4CTR.

翻译：有效的特征交互建模对于提升工业推荐系统中点击率（CTR）预测的准确性至关重要。当前大多数深度CTR模型通过构建复杂的网络架构来更好地捕捉复杂的特征交互或用户行为。然而，我们发现这些模型存在两个局限性：（1）输入模型的样本未经区分，导致模型可能固化为学习大量简单样本而忽略少量困难样本，从而降低模型的泛化能力；（2）设计用以捕获不同交互信息的差异化特征交互编码器接收了一致的监督信号，从而限制了编码器的有效性。为弥补上述不足，本文通过集成即插即用的双焦点（TF）损失函数、样本选择嵌入模块（SSEM）和动态融合模块（DFM），提出了一种名为TF4CTR的新型CTR预测框架。具体而言，该框架在模型底部采用SSEM对样本进行区分，从而为每个样本分配更合适的编码器；同时，TF损失函数为简单编码器和复杂编码器提供定制化的监督信号。此外，DFM动态融合编码器捕获的特征交互信息，从而生成更准确的预测结果。在五个真实世界数据集上的实验验证了该框架的有效性与兼容性，表明其能以模型无关的方式增强多种代表性基线模型的性能。为促进可重复研究，我们将开源代码及详细运行日志公开于：https://github.com/salmon1802/TF4CTR。