通过预测直播未来内容，提升推荐精准度与用户参与度。
Foresight Prediction Enhanced Live-Streaming Recommendation
- 用语义编号编码直播片段，捕捉主播风格
- 预测未来内容趋势，提升推荐相关性
- 适合追求实时互动体验的直播平台
直播作为支持创作者与用户实时互动的新媒体形式，因其内容与时间的高度动态性，对推荐算法提出了更高要求——需实时理解内容变化，并在恰当时机推送。分析发现，用户在直播精彩时刻体验更好、互动更积极。然而，推荐模型在生成推荐时无法获取未来内容，而用户参与度却依赖于后续内容是否契合兴趣。为此，本文对直播片段进行语义量化，生成语义编号（Sid），编码历史Sid序列以捕捉作者特征，并建模Sid演变趋势，实现对未来内容的前瞻性预测。该预测结果用于优化排序模型的特征表示。大量离线与在线实验验证了方法的有效性。
原文摘要 · Abstract (English)
Live-streaming, as an emerging media enabling real-time interaction between authors and users, has attracted significant attention. Unlike the stable playback time of traditional TV live or the fixed content of short video, live-streaming, due to the dynamics of content and time, poses higher requirements for the recommendation algorithm of the platform - understanding the ever-changing content in real time and push it to users at the appropriate moment. Through analysis, we find that users have a better experience and express more positive behaviors during highlight moments of the live-streaming. Furthermore, since the model lacks access to future content during recommendation, yet user engagement depends on how well subsequent content aligns with their interests, an intuitive solution is to predict future live-streaming content. Therefore, we perform semantic quantization on live-streaming segments to obtain Semantic ids (Sid), encode the historical Sid sequence to capture the author's characteristics, and model Sid evolution trend to enable foresight prediction of future content. This foresight enhances the ranking model through refined features. Extensive offline and online experiments demonstrate the effectiveness of our method.
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