Spotify paper: synthetic multi-turn dialogs and self-improvement loops boost conversational recsys quality by 8%
_reachsumit · x · 2026-09-28
Spotify researchers (Enrico Palumbo et al., 18 authors) published an arXiv paper tackling the cold-start problem of conversational recommendation agents — optimizing agent planning (selecting, sequencing and invoking tools) when real user interactions don't exist yet.
Key contributions:
- Synthetic data pipeline: transforms single-turn prompts into realistic multi-turn conversations, enabling systematic pre-launch evaluation.
- Self-improvement loop: combines variance-based contrastive optimization with iterative refinement via a coding agent that automatically finds and fixes planning and tool-use errors.
- Results: +8% quality improvement on top of a highly optimized manual prompt; productionized and significantly accelerated iteration cycles before launch.
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