This paper introduces an LLM-powered agentic recommendation system for Connected TV content discovery. The system addresses the challenge of incorporating diverse contextual signals, such as trending topics and user activities, which traditional multi-stage recommendation systems struggle to process. By leveraging the reasoning capabilities of large language models, the system can naturally synthesize information from various sources and structures, reducing the need for extensive feature engineering. The proposed agentic architecture combines LLM flexibility with the performance of traditional machine learning models to overcome practical limitations like inference latency and improve recommendation tasks. AI
IMPACT This hybrid approach could improve content discovery by better integrating diverse data sources and optimizing LLM performance for recommendation tasks.
RANK_REASON The item is a research paper detailing a novel system architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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