A framework for real-time LLM-based user interest personas in large-scale video recommendations, using distillation, async inference, and video clustering to balance interests with novel topics and improve viewer value via A/B tests.
Chi, Lichan Hong, Ningren Han, and Haokai Lu
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Advocates prioritizing explicit contextual feedback in LLM-based recommender systems to improve user preference alignment and explainability.
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LLM-Based User Personas for Recommendations at Scale
A framework for real-time LLM-based user interest personas in large-scale video recommendations, using distillation, async inference, and video clustering to balance interests with novel topics and improve viewer value via A/B tests.
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Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
Advocates prioritizing explicit contextual feedback in LLM-based recommender systems to improve user preference alignment and explainability.