SSRLive：基于动态语义ID的直播推荐
SSRLive: Live Streaming Recommendation with Dynamic Semantic ID
AI总结：
针对直播推荐中静态语义ID无法反映内容动态变化、生成式方法忽略用户-主播交互信号的问题，提出SSRLive框架，结合生成模块（动态语义ID）与判别模块（交互增强），在真实部署中显著提升观看时长、GMV等指标。
摘要：
直播已成为增长最快的在线媒体形式之一，支持即时内容广播和用户与主播之间的实时互动。尽管现有推荐算法在该领域有效，但它们通常计算资源利用率有限，低FLOPs阻碍了性能进一步提升。生成式推荐技术在各种工业任务中受到关注，为改进直播推荐提供了有前景的途径。然而，直接将生成方法应用于直播并非易事，因为存在两大挑战：（1）静态语义ID无法反映直播房间内容的快速变化；（2）生成式流水线通常不包含用户-主播交互信号（如点赞、订单），而这些信号对于建模用户对主播和展示产品的意图至关重要。为应对这些挑战，我们提出SSRLive：面向直播平台的动态语义ID引导的流式推荐。该框架在统一架构中集成了生成模块和判别模块。生成组件采用编码器-解码器设计，产生静态和动态语义ID，能够及时表示直播房间内容，同时利用多模态信息。判别组件通过将语义ID与用户特征结合来细化任务特定表示，并用用户-主播交互数据增强这些表示，执行多任务预测。实际部署中的在线A/B测试证明了切实的收益：观看时长（+3.38%）、GMV（+0.72%）、粉丝增长（+3.12%）和互动量（+2.92%）。这些改进凸显了SSRLive的有效性和商业价值，该系统现已全面部署，服务于数亿活跃用户。
英文摘要：
Live streaming has emerged as one of the fastest-growing forms of online media, enabling instant content broadcasting and real-time engagement between users and streamers. Despite the effectiveness of existing recommendation algorithms in this domain, they often suffer from limited utilization of computational resources, with low FLOPs that hinder further performance enhancement. Generative recommendation techniques, which have gained traction in various industrial tasks, offer a promising avenue for improving live streaming recommendations. However, directly applying generative methods to live streaming is non-trivial due to two major challenges: (1) static semantic IDs (SIDs) cannot reflect the rapidly changing nature of live room content; and (2) generative pipelines generally do not incorporate user--streamer interaction signals (e.g., likes, orders), which are critical for modeling user intent toward both the streamer and showcased products. To address these challenges, we introduce SSRLive: Dynamic Semantic ID-guided Streaming Recommendation for Live platforms. The proposed framework integrates a generative module and a discriminative module in a unified architecture. The generative component employs an encoder-decoder design to produce both static and dynamic SIDs, enabling timely representation of live room content while leveraging multimodal information. The discriminative component refines task-specific representations by combining SIDs with user features, augments them with user-streamer interaction data, and performs multi-task predictions. Online A/B tests in real-world deployment demonstrate tangible benefits: watch time (+3.38%), GMV (+0.72%), follower growth (+3.12%), and interaction volume (+2.92%). These improvements highlight the effectiveness and business value of SSRLive, which is now fully deployed, serving hundreds of millions of active users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
