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：基于规则与分类的最先进食物推荐系统在实用性与有效性方面面临重大挑战。这一困境主要源于大多数机器学习模型难以处理类别近乎无限、样本数量有限且数据集分布不平衡的问题。另一方面，作为推荐引擎的大语言模型（LLMs）的涌现提供了有前景的解决方案。然而，通用型推荐即语言处理（RLP）方法缺乏实现高效食物推荐的关键组件。为填补这一空白，我们提出食物推荐即语言处理（F-RLP）这一新型框架，其构建了针对食物领域的定制化基础设施。F-RLP通过充分发挥大语言模型的能力，最大程度释放其潜力，从而为更精确、个性化的食物推荐铺平道路。