用生成模型提升搜索广告点击率预测精度。
Generative Click-through Rate Prediction with Applications to Search Advertising
- 先用生成模型预训练用户行为序列的下一步预测
- 再在判别框架中微调，显著提升点击率预测准确率
- 已在大型电商平台落地，适合广告推荐研究者
点击率（CTR）预测模型在个性化搜索广告等工业场景中至关重要。现有方法通常结合用户历史行为特征与商品信息，通过判别模型基于用户反馈估计点击率。随着GPT等模型的成功，生成模型在表达能力上超越判别模型的潜力逐渐显现。为此，我们提出一种新模型，利用生成模型增强判别模型的CTR预测精度。为协调两类模型的数据聚合差异，设计两阶段训练流程：1）以给定物品类别为条件，对用户行为序列进行下一步物品生成预训练；2）将预训练好的生成模型嵌入判别式CTR预测框架中进行微调。大量实验验证了该方法的有效性，线上A/B测试结果进一步证明其显著价值。目前该模型已部署于全球最大的电商之一平台，未来计划公开代码与数据集。
原文摘要 · Abstract (English)
Click-Through Rate (CTR) prediction models are integral to a myriad of industrial settings, such as personalized search advertising. Current methods typically involve feature extraction from users' historical behavior sequences combined with product information, feeding into a discriminative model that is trained on user feedback to estimate CTR. With the success of models such as GPT, the potential for generative models to enrich expressive power beyond discriminative models has become apparent. In light of this, we introduce a novel model that leverages generative models to enhance the precision of CTR predictions in discriminative models. To reconcile the disparate data aggregation needs of both model types, we design a two-stage training process: 1) Generative pre-training for next-item prediction with the given item category in user behavior sequences; 2) Fine-tuning the well-trained generative model within a discriminative CTR prediction framework. Our method's efficacy is substantiated through extensive experiments on a new dataset, and its significant utility is further corroborated by online A/B testing results. Currently, the model is deployed on one of the world's largest e-commerce platforms, and we intend to release the associated code and dataset in the future.
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