让推荐系统实时理解直播内容并匹配用户偏好
LLM-Alignment Live-Streaming Recommendation
- 基于实时语义分析动态捕捉直播内容变化
- 解决用户不同时段观看同一直播体验差异问题
- 适合做直播推荐系统的研发与优化人员
近年来，集成短视频与直播的平台在全球范围内获得广泛应用，支持动态的内容创作与消费。与预录制短视频不同，直播实现了作者与用户之间的实时互动，增强了参与感。然而，这种动态特性给推荐系统（RecSys）带来关键挑战：同一场直播在不同观看时间会产生截然不同的体验。为优化推荐效果，推荐系统必须准确解析直播内容的实时语义，并将其与用户偏好对齐。
原文摘要 · Abstract (English)
In recent years, integrated short-video and live-streaming platforms have gained massive global adoption, offering dynamic content creation and consumption. Unlike pre-recorded short videos, live-streaming enables real-time interaction between authors and users, fostering deeper engagement. However, this dynamic nature introduces a critical challenge for recommendation systems (RecSys): the same live-streaming vastly different experiences depending on when a user watching. To optimize recommendations, a RecSys must accurately interpret the real-time semantics of live content and align them with user preferences.
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