LLM-Based User Personas for Real-Time Recommendations at Scale

Jul 16, 2026

A new framework enables real-time generation of natural-language user interest personas using large language models (LLMs) for a large-scale commercial video recommendation platform. The system addresses the exploitation-exploration trade-off by summarizing user interests and introducing novel topics during serving. To support deployment at billion-user scale, the architecture incorporates knowledge distillation, asynchronous inference, and input optimization. Offline evaluations, user studies, and live A/B tests show significant improvements in viewer value.

Why it matters: This work demonstrates a practical approach for deploying LLMs in real-time personalization at industrial scale, advancing the integration of semantic understanding in recommendation systems.

Full story at: arXiv Information Retrieval ↗