用大模型打造更懂食物的个性化推荐系统
An Integrated Framework for Contextual Personalized LLM-Based Food Recommendation
- 构建专用食物推荐语言处理框架，融合多模态数据与地理信息
- 通过地理定位分析实现独特食物偏好建模，提升推荐精准度
- 适合食品科技、智能健康领域研究者参考
个性化食物推荐系统因组件理解碎片化及传统机器学习在海量不均衡食物数据上的表现不佳而效果受限。尽管大语言模型（LLMs）展现出潜力，但现有通用的推荐即语言处理（RLP）方法缺乏对食物领域复杂性的针对性。本文首先识别并分析了高效食物推荐系统的关键组件，提出两项核心创新：一个用于获取丰富上下文数据的多模态食物记录平台，以及支持独特地理定位食物分析的World Food Atlas。在此基础上，我们开创性地提出专为食物领域设计的食品推荐即语言处理（F-RLP）框架——一种集成化、定制化的解决方案，通过针对性调优大模型，克服通用模型局限，为实现有效、上下文敏感且真正个性化的食物推荐提供坚实基础。
原文摘要 · Abstract (English)
Personalized food recommendation systems (Food-RecSys) critically underperform due to fragmented component understanding and the failure of conventional machine learning with vast, imbalanced food data. While Large Language Models (LLMs) offer promise, current generic Recommendation as Language Processing (RLP) strategies lack the necessary specialization for the food domain's complexity. This thesis tackles these deficiencies by first identifying and analyzing the essential components for effective Food-RecSys. We introduce two key innovations: a multimedia food logging platform for rich contextual data acquisition and the World Food Atlas, enabling unique geolocation-based food analysis previously unavailable. Building on this foundation, we pioneer the Food Recommendation as Language Processing (F-RLP) framework - a novel, integrated approach specifically architected for the food domain. F-RLP leverages LLMs in a tailored manner, overcoming the limitations of generic models and providing a robust infrastructure for effective, contextual, and truly personalized food recommendations.
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