Click-through rate (CTR) prediction plays an important role in personalized recommendations. Recently, sample-level retrieval-based models (e.g., RIM) have achieved remarkable performance by retrieving and aggregating relevant samples. However, their inefficiency at the inference stage makes them impractical for industrial applications. To overcome this issue, this paper proposes a universal plug-and-play Retrieval-Oriented Knowledge (ROK) framework. Specifically, a knowledge base, consisting of a retrieval-oriented embedding layer and a knowledge encoder, is designed to preserve and imitate the retrieved & aggregated representations in a decomposition-reconstruction paradigm. Knowledge distillation and contrastive learning methods are utilized to optimize the knowledge base, and the learned retrieval-enhanced representations can be integrated with arbitrary CTR models in both instance-wise and feature-wise manners. Extensive experiments on three large-scale datasets show that ROK achieves competitive performance with the retrieval-based CTR models while reserving superior inference efficiency and model compatibility.

翻译：点击率（CTR）预测在个性化推荐系统中扮演着重要角色。近年来，基于样本级检索的模型（例如RIM）通过检索并聚合相关样本取得了显著性能。然而，这类模型在推理阶段的低效率使其难以应用于工业场景。为解决这一问题，本文提出一种通用的即插即用型检索导向知识（ROK）框架。具体而言，设计了一个由检索导向嵌入层与知识编码器组成的知识库，通过"分解-重构"范式保留并模仿经检索与聚合后的表征。采用知识蒸馏与对比学习方法优化知识库，最终获得的检索增强表征能够以实例级和特征级两种方式与任意CTR模型集成。在三个大规模数据集上的广泛实验表明，ROK在保持与基于检索的CTR模型相当性能的同时，兼具更优的推理效率与模型兼容性。