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模型致力于构建复杂的网络架构，以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）提供给模型的样本未经区分，这可能导致模型以单一方式学习大量简单样本，而忽略少量困难样本，从而降低模型的泛化能力；（2）为捕捉不同交互信息而设计的差异化特征交互编码器接收到一致的监督信号，从而限制了编码器的有效性。为弥补这些不足，本文提出了一种新颖的CTR预测框架，该框架集成了即插即用的双焦点（TF）损失、样本选择嵌入模块（SSEM）和动态融合模块（DFM），并将其命名为用于CTR预测的双焦点框架（TF4CTR）。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损失为简单和复杂编码器提供定制化的监督信号。此外，DFM动态融合各编码器捕获的特征交互信息，从而实现更精准的预测。在五个真实数据集上的实验验证了该框架的有效性和兼容性，证明了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，我们开源的代码及详细运行日志将在以下地址提供：https://github.com/salmon1802/TF4CTR。