Short-video recommendation is one of the most important recommendation applications in today's industrial information systems. Compared with other recommendation tasks, the enormous amount of feedback is the most typical characteristic. Specifically, in short-video recommendation, the easiest-to-collect user feedback is the skipping behavior, which leads to two critical challenges for the recommendation model. First, the skipping behavior reflects implicit user preferences, and thus, it is challenging for interest extraction. Second, this kind of special feedback involves multiple objectives, such as total watching time and skipping rate, which is also very challenging. In this paper, we present our industrial solution in Kuaishou, which serves billion-level users every day. Specifically, we deploy a feedback-aware encoding module that extracts user preferences, taking the impact of context into consideration. We further design a multi-objective prediction module which well distinguishes the relation and differences among different model objectives in the short-video recommendation. We conduct extensive online A/B tests, along with detailed and careful analysis, which verify the effectiveness of our solution.

翻译：短视频推荐是当今工业信息系统中最重要的推荐应用之一。与其他推荐任务相比，海量的反馈数据是其最典型的特征。具体而言，在短视频推荐中，最易收集的用户反馈是跳过行为，这给推荐模型带来了两个关键挑战：首先，跳过行为反映了用户的隐式偏好，因此兴趣提取具有挑战性；其次，这种特殊反馈涉及多个目标（如总观看时长和跳过率），同样极具挑战性。本文介绍了我们在快手平台上的工业级解决方案，该方案每天服务于数十亿级用户。具体而言，我们部署了一个反馈感知编码模块，在考虑上下文影响的基础上提取用户偏好。我们进一步设计了多目标预测模块，能够清晰区分短视频推荐中不同模型目标之间的关系与差异。我们进行了大规模的在线A/B测试，并结合详细严谨的分析，验证了该方案的有效性。