Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery
cs.AI, cs.IR
Submitted: 2026-09-20
Updated: 2026-09-20
License: http://creativecommons.org/licenses/by/4.0/
The gist: Modern music streaming platforms face a persistent tradeoff: exploiting familiar content versus driving the exploration of novel items.
Terminology
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.
Sources
- Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
- A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
- Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval
- Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
- OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment
- OneRec-V2 Technical Report
- TALKPLAY: Multimodal Music Recommendation with Large Language Models
- Gemini: A Family of Highly Capable Multimodal Models
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