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 from the skipping behaviors, 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, the kind of special feedback involves multiple objectives, such as total watching time, 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 which well extracts user preference 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 testing, along with detailed and careful analysis, which verifies 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，海量反馈是其最典型的特征。具体而言，在短视频推荐中，最易收集的用户反馈来源于跳过行为，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，因此难以进行兴趣提取；其次，这种特殊反馈涉及多个目标（如总观看时长），同样极具挑战性。本文介绍了我们在快手平台上的工业解决方案，该方案每日服务数十亿级用户。具体而言，我们部署了一个感知反馈的编码模块，在考虑上下文影响的情况下充分提取用户偏好。进一步地，我们设计了一个多目标预测模块，能够有效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，并结合详细周密的验证分析，证实了该方案的有效性。