title: KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large
    Language Models
summary: 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.
paper_authors._id: 682dd6a9e3e00ded42a93674
paper_authors.name: Fnu Mohbat
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-21T13:35:37.613Z
paper_authors._id: 615711e6a1fdc424aed1eb48
paper_authors.avatarUrl: /avatars/88279cb7ca39985f618a444128826893.svg
paper_authors.fullname: Fnu Mohbat
paper_authors.type: user
paper_authors.user: mohbattharani
paper_authors._id: 682dd6a9e3e00ded42a93675
paper_authors.name: Mohammed J Zaki
paper_title: KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large
    Language Models
paper_summary: 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.
paper_ai_summary: KERL, integrating food knowledge graphs with large language models, offers personalized food recommendations and recipe generation with nutritional information.
paper_ai_keywords: large language models knowledge graphs KERL recipe generation nutritional analysis