KERL：基于大语言模型的知识增强个性化食谱推荐
KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models
AI总结：
本研究提出融合食品知识图谱与大语言模型的KERL系统，可实现个性化食谱推荐、烹饪步骤生成及营养分析，并构建基准数据集验证其性能优于现有方法。
摘要：
大语言模型（LLM）的最新进展与食品数据的丰富性推动了利用LLM提升食品理解能力的相关研究。尽管已有若干推荐系统结合了LLM与知识图谱（KGs），但针对食品相关KG与LLM融合的研究仍较为有限。我们提出KERL——一个统一系统，借助食品KG与LLM提供个性化食品推荐，并生成附带微量营养信息的食谱。给定自然语言问题，KERL会提取实体，从KG中检索子图，再将子图作为上下文输入LLM，以筛选满足约束条件的食谱。随后，系统为每个食谱生成烹饪步骤与营养信息。为评估该方法，我们还整理了食谱相关问题，结合约束条件与个人偏好，构建了一个基准数据集。通过大量实验表明，我们提出的KG增强型LLM显著优于现有方法，为食品推荐、食谱生成与营养分析提供了完整且连贯的解决方案。我们的代码与基准数据集已在https://github.com/mohbattharani/KERL公开。
英文摘要：
Recent advances in large language models (LLMs) and the abundance of food data have resulted in studies to improve food understanding using LLMs. Despite several recommendation systems utilizing LLMs and Knowledge Graphs (KGs), there has been limited research on integrating food related KGs with LLMs. We introduce KERL, a unified system that leverages food KGs and LLMs to provide personalized food recommendations and generates recipes with associated micro-nutritional information. Given a natural language question, KERL extracts entities, retrieves subgraphs from the KG, which are then fed into the LLM as context to select the recipes that satisfy the constraints. Next, our system generates the cooking steps and nutritional information for each recipe. To evaluate our approach, we also develop a benchmark dataset by curating recipe related questions, combined with constraints and personal preferences. Through extensive experiments, we show that our proposed KG-augmented LLM significantly outperforms existing approaches, offering a complete and coherent solution for food recommendation, recipe generation, and nutritional analysis. Our code and benchmark datasets are publicly available at https://github.com/mohbattharani/KERL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
