Click-Through Rate (CTR) prediction, a core task in recommendation systems, aims to estimate the probability of users clicking on items. Existing models predominantly follow a discriminative paradigm, which relies heavily on explicit interactions between raw ID embeddings. However, this paradigm inherently renders them susceptible to two critical issues: embedding dimensional collapse and information redundancy, stemming from the over-reliance on feature interactions \emph{over raw ID embeddings}. To address these limitations, we propose a novel \emph{Supervised Feature Generation (SFG)} framework, \emph{shifting the paradigm from discriminative ``feature interaction" to generative ``feature generation"}. Specifically, SFG comprises two key components: an \emph{Encoder} that constructs hidden embeddings for each feature, and a \emph{Decoder} tasked with regenerating the feature embeddings of all features from these hidden representations. Unlike existing generative approaches that adopt self-supervised losses, we introduce a supervised loss to utilize the supervised signal, \ie, click or not, in the CTR prediction task. This framework exhibits strong generalizability: it can be seamlessly integrated with most existing CTR models, reformulating them under the generative paradigm. Extensive experiments demonstrate that SFG consistently mitigates embedding collapse and reduces information redundancy, while yielding substantial performance gains across various datasets and base models. The code is available at https://github.com/USTC-StarTeam/GE4Rec.

翻译：点击率（CTR）预测作为推荐系统的核心任务，旨在估计用户点击物品的概率。现有模型主要遵循判别式范式，其高度依赖于原始ID嵌入之间的显式交互。然而，该范式本质上使其易受两个关键问题的影响：嵌入维度坍缩和信息冗余，这源于对原始ID嵌入上特征交互的过度依赖。为应对这些局限，我们提出了一种新颖的监督特征生成（SFG）框架，将范式从判别式的“特征交互”转向生成式的“特征生成”。具体而言，SFG包含两个关键组件：一个为每个特征构建隐藏嵌入的编码器，以及一个从这些隐藏表示中重构所有特征嵌入的解码器。与采用自监督损失的现有生成方法不同，我们引入了监督损失以利用CTR预测任务中的监督信号（即点击与否）。该框架展现出强大的泛化能力：可无缝集成到大多数现有CTR模型中，在生成范式下对其进行重构。大量实验表明，SFG能持续缓解嵌入坍缩并降低信息冗余，同时在多种数据集和基础模型上实现显著的性能提升。代码发布于 https://github.com/USTC-StarTeam/GE4Rec。