用大模型和食物知识图谱，实现个性化食谱推荐与营养分析
KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models
- 结合食物知识图谱与大模型，从自然语言提问中提取实体并检索子图
- 生成包含烹饪步骤和微营养信息的完整食谱，准确率显著提升
- 适合对智能厨艺助手、营养饮食规划感兴趣的开发者与研究者
大型语言模型（LLMs）和丰富食品数据的进步推动了基于LLMs的食品理解研究。尽管已有推荐系统融合了LLMs与知识图谱（KGs），但针对食物相关KGs与LLMs的整合研究仍有限。我们提出KERL，一个统一系统，利用食物KGs和LLMs实现个性化食品推荐，并生成附带微营养信息的食谱。给定自然语言问题，KERL提取实体，从KG中检索子图，并将子图作为上下文输入至LLM，以筛选满足约束条件的食谱。随后，系统生成每道食谱的烹饪步骤及营养信息。为评估该方法，我们还构建了一个基准数据集，涵盖食谱相关问题、约束条件和个人偏好。大量实验表明，所提出的增强型知识图谱的LLM显著优于现有方法，提供了一套完整的食品推荐、食谱生成与营养分析解决方案。代码与基准数据集已公开于https://github.com/mohbattharani/KERL。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。