发表机构
Google LLC（谷歌有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过解耦架构离线预计算LLM理由说明，在YouTube Music上实现大规模可解释推荐，显著提升用户探索和参与度。
AI 中文摘要
现代音乐流媒体平台面临着一个持续的权衡：利用熟悉内容与推动对新项目的探索。虽然用户经常渴望发现，但他们不愿选择不知名艺术家而非经过验证的最爱。提供透明的、自然语言的理由说明，解释为何推荐一个未探索的项目，降低了这一障碍。然而，尽管大型语言模型（LLMs）擅长这种细微的可解释性，其实时部署受到高昂推理成本和计算开销的严重瓶颈限制。在本文中，我们展示了一个解耦推荐架构的行业案例研究，该架构在不牺牲延迟的情况下成功扩展了探索。我们的系统将LLM推理异步离线隔离，预先计算未发现艺术家的个性化候选池以及量身定制的理由说明。大规模在线A/B实验验证了我们的设计。我们证明，将LLM支持的推荐与这些解释性理由说明相结合，显著降低了对新内容的信任障碍，在发现界面上产生了用户探索和整体参与度的统计显著改善。
英文摘要
Modern music streaming platforms face a persistent tradeoff: exploiting familiar content versus driving the exploration of novel items. While users frequently desire discovery, they hesitate to select unknown artists over proven favorites. Providing transparent, natural language rationales that explain why an unexplored item is recommended lowers this barrier. However, while Large Language Models (LLMs) excel at this nuanced explainability, their real-time deployment is severely bottlenecked by prohibitive inference costs and computational overhead. In this paper, we present an industry case study of a decoupled recommendation architecture that successfully scales exploration without compromising latency. Our system isolates LLM inference asynchronously offline, pre-computing personalized candidate pools of undiscovered artists alongside tailored rationales. Large-scale online A/B experiments validate our design. We demonstrate that combining LLM-backed recommendations with these explanatory rationales significantly reduces the trust barrier for new content, yielding statistically significant improvements in both user exploration and overall engagement on the discovery surfaces.
