Recommendation systems (RecSys) are designed to connect users with relevant items from a vast pool of candidates while aligning with the business goals of the platform. A typical industrial RecSys is composed of two main stages, retrieval and ranking: (1) the retrieval stage aims at searching hundreds of item candidates satisfied user interests; (2) based on the retrieved items, the ranking stage aims at selecting the best dozen items by multiple targets estimation for each item candidate, including classification and regression targets. Compared with ranking model, the retrieval model absence of item candidate information during inference, therefore retrieval models are often trained by classification target only (e.g., click-through rate), but failed to incorporate regression target (e.g., the expected watch-time), which limit the effectiveness of retrieval. In this paper, we propose the Controllable Retrieval Model (CRM), which integrates regression information as conditional features into the two-tower retrieval paradigm. This modification enables the retrieval stage could fulfill the target gap with ranking model, enhancing the retrieval model ability to search item candidates satisfied the user interests and condition effectively. We validate the effectiveness of CRM through real-world A/B testing and demonstrate its successful deployment in Kuaishou short-video recommendation system, which serves over 400 million users.

翻译：推荐系统（RecSys）旨在从海量候选项目中连接用户与相关内容，同时与平台的商业目标保持一致。典型的工业级推荐系统由检索和排序两个主要阶段构成：（1）检索阶段旨在搜索数百个满足用户兴趣的候选项目；（2）基于检索出的项目，排序阶段通过多目标估计为每个候选项目（包括分类与回归目标）筛选出最佳的十数个项目。与排序模型相比，检索模型在推理过程中缺乏候选项目信息，因此通常仅通过分类目标（如点击率）进行训练，而未能纳入回归目标（如预期观看时长），这限制了检索的有效性。本文提出可控检索模型（CRM），将回归信息作为条件特征整合到双塔检索范式中。这一改进使检索阶段能够弥补与排序模型的目标差距，增强检索模型根据用户兴趣与条件有效搜索候选项目的能力。我们通过实际A/B测试验证了CRM的有效性，并展示了其在快手短视频推荐系统中的成功部署，该系统服务超过4亿用户。