Live streaming recommender system is specifically designed to recommend real-time live streaming of interest to users. Due to the dynamic changes of live content, improving the timeliness of the live streaming recommender system is a critical problem. Intuitively, the timeliness of the data determines the upper bound of the timeliness that models can learn. However, none of the previous works addresses the timeliness problem of the live streaming recommender system from the perspective of data stream design. Employing the conventional fixed window data stream paradigm introduces a trade-off dilemma between labeling accuracy and timeliness. In this paper, we propose a new data stream design paradigm, dubbed Sliver, that addresses the timeliness and accuracy problem of labels by reducing the window size and implementing a sliding window correspondingly. Meanwhile, we propose a time-sensitive re-reco strategy reducing the latency between request and impression to improve the timeliness of the recommendation service and features by periodically requesting the recommendation service. To demonstrate the effectiveness of our approach, we conduct offline experiments on a multi-task live streaming dataset with labeling timestamps collected from the Kuaishou live streaming platform. Experimental results demonstrate that Sliver outperforms two fixed-window data streams with varying window sizes across all targets in four typical multi-task recommendation models. Furthermore, we deployed Sliver on the Kuaishou live streaming platform. Results of the online A/B test show a significant improvement in click-through rate (CTR), and new follow number (NFN), further validating the effectiveness of Sliver.

翻译：直播推荐系统专用于向用户推荐其感兴趣的实时直播内容。由于直播内容的动态变化特性，提升直播推荐系统的时效性是一个关键问题。直观而言，数据的时效性决定了模型能够学习的时效性上限。然而，现有研究均未从数据流设计的角度解决直播推荐系统的时效性问题。采用传统的固定窗口数据流范式会在标注准确性与时效性之间引入权衡困境。本文提出一种名为Sliver的新型数据流设计范式，通过缩减窗口尺寸并相应采用滑动窗口机制，解决了标签的时效性与准确性问题。同时，我们提出一种时间敏感的重新推荐策略，通过周期性请求推荐服务来降低请求与展示之间的延迟，从而提升推荐服务与特征的时效性。为验证方法的有效性，我们在包含标注时间戳的多任务直播数据集上开展离线实验，该数据集来自快手直播平台。实验结果表明，在四种典型多任务推荐模型的所有目标指标上，Sliver均优于两种不同窗口尺寸的固定窗口数据流方案。此外，我们在快手直播平台上部署了Sliver。在线A/B测试结果显示，点击率（CTR）与新关注数（NFN）均获得显著提升，进一步验证了Sliver的有效性。