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.

提出了一种针对实时直播推荐系统的数据流设计范式 Sliver，以解决数据标签的及时性和准确性问题，通过减小窗口大小和滑动窗口相应地实现，同时提出了一种时间敏感的重新推荐策略，通过定期请求推荐服务来减少请求和展示之间的延迟，从而提高推荐服务和特征的及时性，实验结果证明 Sliver 在多个典型的多任务推荐模型中在所有指标上均优于两个具有不同窗口大小的固定窗口数据流。在快手直播平台上部署 Sliver 后，在在线 A/B 测试中展现出显著提升的点击率（CTR）和新关注人数（NFN），进一步验证了 Sliver 的有效性。