An Integrated Framework for Contextual Personalized LLM-Based Food Recommendation

• URL: http://arxiv.org/abs/2504.20092v1
• Date: Fri, 25 Apr 2025 22:20:33 GMT
• Title: An Integrated Framework for Contextual Personalized LLM-Based Food Recommendation
• Authors: Ali Rostami,
• Abstract summary: This thesis identifies and analyzes the essential components for effective Food-RecSys.<n>We introduce two key innovations: a multimedia food logging platform for rich contextual data acquisition and the World Food Atlas.<n>We pioneer the Food Recommendation as Language Processing framework - a novel, integrated approach specifically architected for the food domain.
• Score: 1.4957306171002251
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Personalized food recommendation systems (Food-RecSys) critically underperform due to fragmented component understanding and the failure of conventional machine learning with vast, imbalanced food data. While Large Language Models (LLMs) offer promise, current generic Recommendation as Language Processing (RLP) strategies lack the necessary specialization for the food domain's complexity. This thesis tackles these deficiencies by first identifying and analyzing the essential components for effective Food-RecSys. We introduce two key innovations: a multimedia food logging platform for rich contextual data acquisition and the World Food Atlas, enabling unique geolocation-based food analysis previously unavailable. Building on this foundation, we pioneer the Food Recommendation as Language Processing (F-RLP) framework - a novel, integrated approach specifically architected for the food domain. F-RLP leverages LLMs in a tailored manner, overcoming the limitations of generic models and providing a robust infrastructure for effective, contextual, and truly personalized food recommendations.

Related papers

• Personalized Class Incremental Context-Aware Food Classification for Food Intake Monitoring Systems [3.8767314375943918]
  Existing class-incremental food classification models have low accuracy for the new classes and lack personalization.<n>This paper introduces a personalized, class-incremental food classification model designed to overcome these challenges.<n>Our approach adapts itself to the new array of food classes, maintaining applicability and accuracy, both for new and existing classes by using personalization.
  arXiv  Detail & Related papers  (2025-03-09T14:50:56Z)
• NutriGen: Personalized Meal Plan Generator Leveraging Large Language Models to Enhance Dietary and Nutritional Adherence [6.937243101289335]
  NutriGen is a framework designed to generate personalized meal plans that align with user-defined dietary preferences and constraints. Our evaluation shows that Llama 3.1 8B and GPT-3.5 Turbo achieve the lowest percentage errors of 1.55% and 3.68%, respectively.
  arXiv  Detail & Related papers  (2025-02-28T00:05:49Z)
• MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation [50.309987904297415]
  Major food recommendation platforms such as Yelp prioritize users' dietary preferences over the healthiness of their choices.<n>We develop a novel framework, Multi-Objective Personalized Interpretable Health-aware Food Recommendation System (MOPI-HFRS)<n>It provides food recommendations by jointly optimizing the three objectives: user preference, personalized healthiness and nutritional diversity, along with a large language model (LLM)-enhanced reasoning module.
  arXiv  Detail & Related papers  (2024-12-12T01:02:09Z)
• RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models [96.43285670458803]
  Uni-Food is a unified food dataset that comprises over 100,000 images with various food labels.<n>Uni-Food is designed to provide a more holistic approach to food data analysis.<n>We introduce a novel Linear Rectification Mixture of Diverse Experts (RoDE) approach to address the inherent challenges of food-related multitasking.
  arXiv  Detail & Related papers  (2024-07-17T16:49:34Z)
• FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination [37.11551779015218]
  We introduce Food-oriented Large Language Models (LLMs) to comprehend food data through perception and reasoning. Considering the complexity and typicality of Chinese cuisine, we first construct one comprehensive Chinese food corpus FoodEarth. We then propose Topic-based Selective State Space Model (TS3M) and the Hierarchical Topic Retrieval Augmented Generation (HTRAG) mechanism to enhance FoodSky.
  arXiv  Detail & Related papers  (2024-06-11T01:27:00Z)
• Food Recommendation as Language Processing (F-RLP): A Personalized and Contextual Paradigm [3.0057566758557974]
  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 Large Language Models (LLMs) to maximize their potential, thereby paving the way for more accurate, personalized food recommendations.
  arXiv  Detail & Related papers  (2024-02-12T08:32:29Z)
• FoodLMM: A Versatile Food Assistant using Large Multi-modal Model [96.76271649854542]
  Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities. We introduce a series of novel task-specific tokens and heads, enabling the model to predict food nutritional values and multiple segmentation masks.
  arXiv  Detail & Related papers  (2023-12-22T11:56:22Z)
• FIRE: Food Image to REcipe generation [10.45344523054623]
  Food computing aims to develop end-to-end intelligent systems capable of autonomously producing recipe information for a food image. This paper proposes FIRE, a novel methodology tailored to recipe generation in the food computing domain. We showcase two practical applications that can benefit from integrating FIRE with large language model prompting.
  arXiv  Detail & Related papers  (2023-08-28T08:14:20Z)
• UMDFood: Vision-language models boost food composition compilation [26.5694236976957]
  We propose a novel vision-language model, UMDFood-VL, using front-of-package labeling and product images to accurately estimate food composition profiles. Up to 82.2% of selected products' estimated error between chemical analysis results and model estimation results are less than 10%. This performance sheds light on generalization towards other food and nutrition-related data compilation and catalyzation.
  arXiv  Detail & Related papers  (2023-05-18T03:18:12Z)
• Learning Structural Representations for Recipe Generation and Food Retrieval [101.97397967958722]
  We propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task. Our proposed model can produce high-quality and coherent recipes, and achieve the state-of-the-art performance on the benchmark Recipe1M dataset.
  arXiv  Detail & Related papers  (2021-10-04T06:36:31Z)
• Towards Building a Food Knowledge Graph for Internet of Food [66.57235827087092]
  We review the evolution of food knowledge organization, from food classification to food to food knowledge graphs. Food knowledge graphs play an important role in food search and Question Answering (QA), personalized dietary recommendation, food analysis and visualization. Future directions for food knowledge graphs cover several fields such as multimodal food knowledge graphs and food intelligence.
  arXiv  Detail & Related papers  (2021-07-13T06:26:53Z)
• Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism [70.85894675131624]
  We learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. We propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities through aligning output semantic probabilities. We show that we can outperform several state-of-the-art cross-modal retrieval strategies for food images and cooking recipes by a significant margin.
  arXiv  Detail & Related papers  (2020-03-09T07:41:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.