  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.
