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Spotify
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Grounding LLM evaluations in historical user behavior can boost relevance judgment accuracy by over 15%, making them more aligned with actual user preferences.
Forget full retraining: intelligently selecting data subsets using gradient-based representations can keep your generative recommender fresh and robust to drift.
Forget tool-augmented systems: NEO shows you can consolidate search, recommendation, and reasoning into a single language-steerable LLM by representing items as SIDs and interleaving them with natural language.
Spotify's GLIDE model proves that generative LLMs can drive significant gains in podcast discovery and non-habitual listening in a real-world, production environment.
