Abstract: Click-Through Rate (CTR) prediction is a core task in recommender systems and online advertising, and its performance highly depends on effective feature interaction modeling. Existing methods suffer from several limitations when modeling higher-order feature interactions, including the neglect of domain-level semantic information, the introduction of redundant noise by higher-order interactions, and excessive sharing of input feature representations, which jointly restrict further performance improvement. To address these issues, this paper proposes a CTR prediction model that integrates gated field-aware interactions with soft feature selection. Specifically, a soft feature selection layer is first employed to adaptively reweight embedded features through continuous learnable weights, enabling better adaptation to different interaction networks. Then, a field-aware interaction module is introduced to explicitly model higher-order feature interactions at the field level, so as to preserve domain-level semantic information. Meanwhile, an information gating component is incorporated to dynamically filter key interaction features, effectively suppressing redundant noise. Experimental results on four public datasets, including Criteo, Avazu, MovieLens, and Frappe, show that the proposed model achieves consistent improvements in terms of AUC and LogLoss. For example, compared with the best-performing baseline methods on each dataset, the proposed model improves AUC by 0.12% and 0.13% and reduces LogLoss by 0.11% and 0.14% on Criteo and Avazu, respectively, while maintaining comparable model parameter size and training efficiency. These results demonstrate that the proposed model achieves a favorable balance between prediction accuracy and computational efficiency, indicating strong potential for practical applications.

摘要： 点击率(Click-Through Rate，CTR)预测是推荐系统和在线广告中的核心任务，其性能高度依赖于有效的特征交互建模。现有方法在建模高阶交互的过程中存在忽视域级语义信息的问题，同时高阶交互引入的冗余噪声以及输入特征的过度共享进一步限制了预测的性能。针对上述问题，提出了一种融合门控域感知交互与特征软选择的点击率预测模型。该模型首先引入特征软选择层，通过连续可学习权重对嵌入特征的重要性进行自适应调节，使其更好地适配不同的交互网络；随后采用门控域感知交互模块在域级尺度上显式建模高阶特征交互关系，以保留域级语义信息；同时，利用信息门组件动态筛选关键交互特征，从而有效抑制冗余噪声。在 Criteo、Avazu、MovieLens 和 Frappe 四个公开数据集上的实验结果表明，所提出模型在 AUC 和 LogLoss 指标上均取得稳定提升。以 Criteo 和 Avazu 为例，模型相比最优的对比方法在 AUC 上分别提升 0.12% 和 0.13%，在 LogLoss 上分别降低 0.11% 和 0.14%，其余数据集上亦取得一致性改进。同时，在模型参数规模与训练效率方面，本方法保持了与强基线模型相当的计算开销。实验结果验证了该模型在预测精度与计算效率之间能够取得良好平衡，具有较高的实际应用价值。