用离散扩散模型生成点击率预测所需交叉特征，提升冷启动场景表现
DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion
- 基于离散扩散模型生成用户-物品交叉特征，替代传统手工构造
- 两阶段训练：先生成预训练，再针对点击率微调，性能显著优于基线
- 特别适合标签稀疏场景，适用于推荐系统冷启动与数据不足问题
生成模型的进展启发了推荐系统探索生成式方法，但现有研究多聚焦于序列生成，不适用于点击率（CTR）预测。CTR模型高度依赖目标物品与用户之间的大量交叉特征来估计点击概率，若丢弃这些特征将严重损害模型性能。为此，我们提出一种专为CTR任务设计的样本级生成范式：基于离散扩散的两阶段生成式CTR训练框架（DGenCTR）。该框架包含一个基于扩散的生成预训练阶段和一个面向CTR的监督微调阶段。大量离线实验与线上A/B测试结果充分验证了该框架的有效性。
原文摘要 · Abstract (English)
Recent advances in generative models have inspired the field of recommender systems to explore generative approaches, but most existing research focuses on sequence generation, a paradigm ill-suited for click-through rate (CTR) prediction. CTR models critically depend on a large number of cross-features between the target item and the user to estimate the probability of clicking on the item, and discarding these cross-features will significantly impair model performance. Therefore, to harness the ability of generative models to understand data distributions and thereby alleviate the constraints of traditional discriminative models in label-scarce space, diverging from the item-generation paradigm of sequence generation methods, we propose a novel sample-level generation paradigm specifically designed for the CTR task: a two-stage Discrete Diffusion-Based Generative CTR training framework (DGenCTR). This two-stage framework comprises a diffusion-based generative pre-training stage and a CTR-targeted supervised fine-tuning stage for CTR. Finally, extensive offline experiments and online A/B testing conclusively validate the effectiveness of our framework.
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