User preferences follow a dynamic pattern over a day, e.g., at 8 am, a user might prefer to read news, while at 8 pm, they might prefer to watch movies. Time modeling aims to enable recommendation systems to perceive time changes to capture users' dynamic preferences over time, which is an important and challenging problem in recommendation systems. Especially, streaming recommendation systems in the industry, with only available samples of the current moment, present greater challenges for time modeling. There is still a lack of effective time modeling methods for streaming recommendation systems. In this paper, we propose an effective and universal method Interest Clock to perceive time information in recommendation systems. Interest Clock first encodes users' time-aware preferences into a clock (hour-level personalized features) and then uses Gaussian distribution to smooth and aggregate them into the final interest clock embedding according to the current time for the final prediction. By arming base models with Interest Clock, we conduct online A/B tests, obtaining +0.509% and +0.758% improvements on user active days and app duration respectively. Besides, the extended offline experiments show improvements as well. Interest Clock has been deployed on Douyin Music App.

翻译：用户偏好随时间呈现动态变化模式，例如上午8点可能偏好阅读新闻，而晚上8点则可能倾向观看电影。时间建模旨在使推荐系统具备时间变化感知能力，从而捕获用户随时间变化的动态偏好，这是推荐系统中重要且富有挑战性的问题。尤其工业界的流式推荐系统仅能获取当前时刻的样本，为时间建模带来了更大挑战。目前针对流式推荐系统仍缺乏有效的时间建模方法。本文提出一种高效且通用的方法——兴趣时钟（Interest Clock），用于感知推荐系统中的时间信息。兴趣时钟首先将用户的时间感知偏好编码为时钟（小时级的个性化特征），然后通过高斯分布根据当前时间对其平滑聚合，形成最终的兴趣时钟嵌入以进行预测。通过在基础模型上搭载兴趣时钟，我们在线上A/B测试中获得用户活跃天数提升+0.509%、应用使用时长提升+0.758%的效果。此外，扩展的离线实验也展示了性能改进。兴趣时钟已在抖音音乐APP中部署上线。