For multi-stage recommenders in industry, a user request would first trigger a simple and efficient retriever module that selects and ranks a list of relevant items, then the recommender calls a slower but more sophisticated reranking model that refines the item list exposure to the user. To consistently optimize the two-stage retrieval reranking framework, most efforts have focused on learning reranker-aware retrievers. In contrast, there has been limited work on how to achieve a retriever-aware reranker. In this work, we provide evidence that the retriever scores from the previous stage are informative signals that have been underexplored. Specifically, we first empirically show that the reranking task under the two-stage framework is naturally a noise reduction problem on the retriever scores, and theoretically show the limitations of naive utilization techniques of the retriever scores. Following this notion, we derive an adversarial framework DNR that associates the denoising reranker with a carefully designed noise generation module. The resulting DNR solution extends the conventional score error minimization loss with three augmented objectives, including: 1) a denoising objective that aims to denoise the noisy retriever scores to align with the user feedback; 2) an adversarial retriever score generation objective that improves the exploration in the retriever score space; and 3) a distribution regularization term that aims to align the distribution of generated noisy retriever scores with the real ones. We conduct extensive experiments on three public datasets and an industrial recommender system, together with analytical support, to validate the effectiveness of the proposed DNR.

翻译：在工业界的多阶段推荐系统中，用户请求首先触发一个简单高效的检索模块，该模块选择并排序一组相关物品，随后推荐系统调用一个更慢但更复杂的重排序模型，以优化向用户展示的物品列表。为了持续优化两阶段检索-重排序框架，大多数研究聚焦于学习感知重排序器的检索器。相比之下，关于如何实现感知检索器的重排序器的研究有限。本文提供证据表明，前一阶段的检索器分数是尚未充分探索的信息信号。具体而言，我们首先通过实验证明，两阶段框架下的重排序任务本质上是对检索器分数进行去噪的问题，并从理论上揭示了朴素利用检索器分数技术的局限性。基于这一观点，我们推导出一个对抗性框架DNR，将去噪重排序器与精心设计的噪声生成模块关联起来。该DNR方案将传统的分数误差最小化损失扩展为三个增强目标，包括：1）一个去噪目标，旨在对噪声检索器分数进行去噪以与用户反馈对齐；2）一个对抗性检索器分数生成目标，改善检索器分数空间的探索；3）一个分布正则化项，使生成的噪声检索器分数的分布与真实分布对齐。我们在三个公开数据集和一个工业推荐系统上进行了广泛实验，并结合分析支持，验证了所提出的DNR的有效性。