Problem
Research questions and friction points this paper is trying to address.
music streaming platforms
exploration-exploitation tradeoff
Large Language Models
inference costs
user engagement
Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery
🤖 AI Summary
为解决音乐流媒体平台中用户对新艺术家的探索犹豫问题，本文提出一种分离推荐架构，利用大语言模型预计算个性化推荐理由，降低用户信任障碍。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decoupled Recommendation Architecture
Large Language Models (LLMs)
Asynchronous Offline Inference
Personalized Candidate Pools
Explanatory Rationales
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