Melo: A Production-Scale LLM-Powered Music Recommendation Agent Deployed by NetEase Cloud Music
A new arXiv preprint describes Melo, a large language model-powered music recommendation agent deployed at scale on NetEase Cloud Music. The system uses a deterministic state graph and introduces inference-time entity grounding and reflective retry mechanisms to address entity hallucination and long-tail recommendation issues. In a month-long online A/B test, Melo achieved over a 2 percentage point increase in playlist retention and more than a one-minute increase in user engagement.
Why it matters: This work demonstrates the real-world deployment and measurable impact of LLM-based agents in a major consumer music platform, highlighting the importance of robust error recovery mechanisms for industrial-scale AI applications.
Full story at: arXiv Information Retrieval ↗