Click-Through Rate (CTR) prediction is a pivotal task in product and content recommendation, where learning effective feature embeddings is of great significance. However, traditional methods typically learn fixed feature representations without dynamically refining feature representations according to the context information, leading to suboptimal performance. Some recent approaches attempt to address this issue by learning bit-wise weights or augmented embeddings for feature representations, but suffer from uninformative or redundant features in the context. To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA. DELTA contains two key components: (I) conscious truncation module (CTM), which utilizes curriculum learning to apply adaptive truncation on attention weights to select the most critical feature in the context; (II) explicit embedding optimization (EEO), which applies an auxiliary task during training that directly and independently propagates the gradient from the loss layer to the embedding layer, thereby optimizing the embedding explicitly via linear feature crossing. Extensive experiments on five challenging CTR datasets demonstrate that DELTA achieves new state-of-art performance among current CTR methods.

翻译：点击率（CTR）预测是产品与内容推荐中的关键任务，其中学习有效的特征嵌入具有重要意义。然而，传统方法通常学习固定的特征表示，无法根据上下文信息动态优化特征表征，导致性能次优。近年部分研究尝试通过逐位权重学习或增强型特征嵌入来解决此问题，但常受限于上下文中的无效或冗余特征。为应对这一挑战，受意识处理中的全局工作空间理论启发——该理论指出仅产品特征的特定子集与人类点击行为相关，其余特征可能产生噪音甚至干扰——我们提出了一种名为DELTA的CTR预测模型，该模型通过动态嵌入学习与截断式意识注意力实现点击率预测。DELTA包含两个核心模块：（I）意识截断模块（CTM）：利用课程学习对注意力权重进行自适应截断，以选择上下文中最关键的特征；（II）显式嵌入优化（EEO）：在训练中引入辅助任务，使损失层梯度直接且独立地传播至嵌入层，从而通过线性特征交叉显式优化嵌入表示。在五个具有挑战性的CTR数据集上的大量实验表明，DELTA在现有CTR方法中取得了当前最佳性能。