Personalization in the Era of LLMs - Shivam Verma, Spotify
May 19, 2026 · 20:12
Shivam Verma of Spotify's AI foundation explains how Spotify personalizes recommendations using open-weight LLMs, user embeddings from 750 million accounts, and Semantic IDs that compress each of 100 million tracks into six hierarchical tokens, enabling autoregressive next-item prediction. Three components: transformer-based user embeddings, Semantic IDs (e.g., Ariana Grande and Bruno Mars share first two tokens as pop artists), and a soft tokenization layer projecting user vectors into the LLM's token space for personalization without fine-tuning. The system is productionized for podcast next-episode recommendations. Verma also introduces Taste Profile, allowing users to edit their taste profile via natural language for steerable recommendations. This moves from traditional multi-stage pipelines to a unified generative model.
