State-of-the-art rule-based and classification-based food recommendation systems face significant challenges in becoming practical and useful. This difficulty arises primarily because most machine learning models struggle with problems characterized by an almost infinite number of classes and a limited number of samples within an unbalanced dataset. Conversely, the emergence of Large Language Models (LLMs) as recommendation engines offers a promising avenue. However, a general-purpose Recommendation as Language Processing (RLP) approach lacks the critical components necessary for effective food recommendations. To address this gap, we introduce Food Recommendation as Language Processing (F-RLP), a novel framework that offers a food-specific, tailored infrastructure. F-RLP leverages the capabilities of LLMs to maximize their potential, thereby paving the way for more accurate, personalized food recommendations.

翻译：基于规则和分类的现代食品推荐系统在实现实用性和有效性方面面临重大挑战。这一困难主要源于大多数机器学习模型难以处理类别数量近乎无限、且样本在非平衡数据集中有限的问题。与此同时，大型语言模型(LLMs)作为推荐引擎的兴起提供了一个有前景的途径。然而，通用的推荐即语言处理(RLP)方法缺乏有效进行食品推荐所必需的关键组件。为解决这一空白，我们提出了食品推荐即语言处理(F-RLP)——一种面向食品的定制化基础设施新框架。F-RLP充分利用LLMs的能力以最大化其潜力，从而为更准确、个性化的食品推荐铺平道路。