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Meta（Meta）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对推荐系统整合上下文信号的挑战，提出用于智能电视内容发现的大语言模型驱动的智能推荐系统，利用其推理能力处理多样信号，采用智能架构协调组件，克服大语言模型用于推荐的实际限制。
AI 中文摘要
推荐系统在整合多样化上下文信号时面临挑战，传统系统缺乏处理非结构化或异构格式信息的推理能力。本文提出一种用于智能电视内容发现的由大语言模型驱动的智能推荐系统，利用大语言模型推理能力处理多样信号，采用智能架构协调组件。虽当前基于大语言模型的解决方案在某些推荐任务上仍不及传统机器学习模型，但该工作成功克服了大语言模型用于推荐的实际限制，分享了见解并讨论了构建混合系统的权衡与经验教训。
英文摘要
Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topics, breaking news, cultural events, and cross-surface user activities, into their ranking pipelines. These systems are designed to consume structured behavioral signals with consistent schemas, and lack the reasoning capability to naturally process unstructured or heterogeneously formatted contextual information. Incorporating such signals typically requires feature engineering, bespoke data pipelines, and carefully tuned heuristics. In this paper, we present an LLM-powered agentic recommendation system designed for Connected TV (CTV) content discovery that addresses these limitations. Our system leverages the reasoning capabilities of large language models to naturally process and synthesize diverse signals across varying schemas and structures, eliminating much of the manual integration inherent in traditional ranking and retrieval systems. Recognizing that current LLM-based solutions still fall short of traditional machine learning models in several recommendation tasks, including retrieval efficiency, personalization precision, and scalability, we adopt an agentic architecture that orchestrates specialized components, allowing each sub-task to be handled by the most suitable method, whether LLM-based or traditional ML. The main contribution of this work is our engineering approach to successfully overcoming the practical limitations of enabling LLM for recommendation, particularly inference latency. We share insights from our work and discuss the trade-offs and lessons learned in building a hybrid system that combines the flexibility of LLMs with the performance of established recommendation techniques.
Comments13 pages, 3 figures
