Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery
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 comp...
| Authors | Xiao Liu, Yanwei Song, Srivaths Ranganathan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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