In recommendation systems, the matching stage is becoming increasingly critical, serving as the upper limit for the entire recommendation process. Recently, some studies have started to explore the use of multi-scenario information for recommendations, such as model-based and data-based approaches. However, the matching stage faces significant challenges due to the need for ultra-large-scale retrieval and meeting low latency requirements. As a result, the methods applied at this stage (collaborative filtering and two-tower models) are often designed to be lightweight, hindering the full utilization of extensive information. On the other hand, the ranking stage features the most sophisticated models with the strongest scoring capabilities, but due to the limited screen size of mobile devices, most of the ranked results may not gain exposure or be displayed. In this paper, we introduce an innovative multi-scenario nearline retrieval framework. It operates by harnessing ranking logs from various scenarios through Flink, allowing us to incorporate finely ranked results from other scenarios into our matching stage in near real-time. Besides, we propose a streaming scoring module, which selects a crucial subset from the candidate pool. Implemented on the "Guess You Like" (homepage of the Taobao APP), China's premier e-commerce platform, our method has shown substantial improvements-most notably, a 5% uptick in product transactions. Furthermore, the proposed approach is not only model-free but also highly efficient, suggesting it can be quickly implemented in diverse scenarios and demonstrate promising performance.

翻译：在推荐系统中，匹配阶段正变得日益关键，它构成了整个推荐过程的上限。近年来，一些研究开始探索利用多场景信息进行推荐，例如基于模型和基于数据的方法。然而，由于需要超大规模检索并满足低延迟要求，匹配阶段面临着重大挑战。因此，应用于该阶段的方法（协同过滤和双塔模型）通常被设计得较为轻量，这阻碍了对海量信息的充分利用。另一方面，排序阶段拥有最复杂的模型和最强的打分能力，但由于移动设备屏幕尺寸有限，大部分排序结果可能无法获得曝光或展示。本文介绍了一种创新的多场景近线检索框架。该框架通过Flink利用来自不同场景的排序日志，使我们能够以近乎实时的方式将其他场景的精细排序结果纳入匹配阶段。此外，我们提出了一个流式打分模块，用于从候选池中筛选出关键子集。该方法已在中国领先的电商平台淘宝APP的“猜你喜欢”（首页）上实施，并显示出显著的改进——最值得注意的是，商品交易量提升了5%。此外，所提出的方法不仅是无模型的，而且效率极高，这表明它可以快速部署于多样化场景并展现出良好的性能。