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ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework

Zhongqi Yang, Elahe Khatibi, Nitish Nagesh, Mahyar Abbasian, Iman Azimi, Ramesh Jain, Amir M. Rahmani

arXiv:2403.00781v3cs.IRcs.AIcs.LGcs.MM

TL;DR

Conventional nutrition recommendation systems often lack personalization, explainability, and interactivity, while standalone LLMs do not adequately use personal data. ChatDiet combines personal and population models through an orchestrator to provide nutrition recommendations, with a reported 92% effectiveness rate in its food recommendation test.

  • Problem

    Conventional nutrition recommendation services often lack personalization, explainability, and interactivity, while nutrition effects for individuals must be inferred from personal data.

    Source: §I · p. 2, B. Exploiting LLMs for Recommendation… · p. 3

  • Method

    ChatDiet combines personal and population models with an orchestrator that retrieves and organizes relevant information for an LLM to generate recommendations.

    Source: §I · p. 2, C. Population Model · p. 6, D. Generative Response · p. 6

  • Results

    92% effectiveness rate in food recommendations was reported for ChatDiet.

    Source: §VI · p. 9

  • Takeaways & Limitations

    Dialogue examples show recommendations grounded in personal nutrition effects as explainable, personalized, and interactive.

    Source: D. ChatDiet’s Personalization… · p. 7, §V · p. 8

    Personalized suggestions are confined to factors and health outcomes available in a single-subject dataset from a limited set of devices; unclear factor names can sometimes lead to inaccurate recommendations.

    Source: §V · p. 8

Abstract

from arXiv · show

The profound impact of food on health necessitates advanced nutrition-oriented food recommendation services. Conventional methods often lack the crucial elements of personalization, explainability, and interactivity. While Large Language Models (LLMs) bring interpretability and explainability, their standalone use falls short of achieving true personalization. In this paper, we introduce ChatDiet, a novel LLM-powered framework designed specifically for personalized nutrition-oriented food recommendation chatbots. ChatDiet integrates personal and population models, complemented by an orchestrator, to seamlessly retrieve and process pertinent information. The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content. The orchestrator retrieves, synergizes and delivers the output of both models to the LLM, providing tailored food recommendations designed to support targeted health outcomes. The result is a dynamic delivery of personalized and explainable food recommendations, tailored to individual user preferences. Our evaluation of ChatDiet includes a compelling case study, where we establish a causal personal model to estimate individual nutrition effects. Our assessments, including a food recommendation test showcasing a 92\% effectiveness rate, coupled with illustrative dialogue examples, underscore ChatDiet's strengths in explainability, personalization, and interactivity.

I. INTRODUCTION

Nutrition-oriented recommendations address health as well as preference, but existing systems often rely on population standards and offer limited personalization, explainability, and interactivity. ChatDiet combines personal and population models with an orchestrator and an LLM to provide personalized nutrition-oriented recommendations.

I. INTRODUCTION

  • Conventional nutrition-oriented services struggle to align recommendations with individual nutritional needs and may overlook variation in people’s responses to nutrition.Source: §I · p. 1
  • Existing services often lack explanations tailored to individual nutritional needs and cannot dynamically adapt to user feedback, new preferences, or status changes.Source: §I · pp. 1–2
  • LLMs can provide interpretable, explainable chatbot recommendations, but alone they do not integrate personal data sufficiently for nutrition-oriented personalization.Source: §I · p. 2
  • ChatDiet combines personal and population models with an orchestrator that supplies relevant information to an LLM for personalized food recommendations.The personal model uses causal discovery and inference to estimate nutrition effects for an individual; the paper also presents a case study and recommendation test.Source: §I · p. 2

II. BACKGROUND AND RELATED WORK

  • The background section outlines nutrition-oriented recommendation research and implementations of LLMs on recommendation tasks.Source: §II · p. 2

A. Nutrition-oriented Food Recommendation

Prior nutrition recommendation work uses nutritional, health, and user-context information, yet individual responses and the need for understandable, adaptable recommendations remain challenges. Existing LLM recommendation approaches also face limits in representing personal data while preserving explainability.

A. Nutrition-oriented Food Recommendation

  • Nutrition-oriented recommendation methods prioritize nutritional content, and prior work has addressed disease-specific needs, healthy populations, and food knowledge modeling.Examples include recommendations for chronic kidney disease, diabetes, and older adults, as well as meal planning and knowledge-graph approaches.Source: A. Nutrition-oriented Food… · p. 2
  • Shared health objectives may require different diets because genetics, behavior, lifestyle, and preferences contribute to individual variation in food responses.Source: A. Nutrition-oriented Food… · p. 2
  • Nutrition recommendation models often provide limited explanations, making it difficult to understand why a recommendation fits an individual’s nutritional needs.The cited exception provides feature-importance insights, while the models discussed generally remain difficult to interpret.Source: A. Nutrition-oriented Food… · p. 2
  • Nutrition recommendation services also generally lack adaptive responses to feedback, new preferences, and changes in user status.Source: A. Nutrition-oriented Food… · p. 2

B. Exploiting LLMs for Recommendation Tasks

  • LLM recommendation research uses models as central recommenders or facilitators, including textual prompts that encode personal data and recommendation tasks.Approaches include prompt templates, instruction tuning, and embeddings derived from user and item representations.Source: B. Exploiting LLMs for Recommendation… · pp. 2–3
  • LLM methods can miss meaningful representations of personal data, while abstract embeddings may compromise explainability; nutrition effects also require inference from personal data.The paper notes that retrieval-augmented generation cannot draw on pre-existing resources in this setting.Source: B. Exploiting LLMs for Recommendation… · p. 3

III. CHATDIET: AN LLM-BASED FRAMEWORK FOR NUTRITION-ORIENTED FOOD RECOMMENDATION

ChatDiet augments an LLM with personal and population information retrieved and organized by an orchestrator. Its case study uses longitudinal individual data and synthetic participants to examine personalized nutrition effects.

III. CHATDIET: AN LLM-BASED FRAMEWORK FOR NUTRITION-ORIENTED FOOD RECOMMENDATION

  • ChatDiet’s orchestrator retrieves relevant information from individual-specific and population models, aggregates it, and sends it to an LLM for user interactions.Figure 1 is identified as an overview of the framework’s realization on N-of-1 data.Source: §III · p. 3, Figure 1 · p. 4

A. Case Study

  • The case study examines links among diet, physical activity, sleep, and health outcomes using one individual’s data collected over three years.Source: A. Case Study · p. 3

1) Dataset:

  • The N-of-1 dataset combines wearable and scale measurements with food logs and health metrics recorded across overlapping periods from 2018 to 2022.Measures include sleep, activity, body composition, meal calories and macronutrients, height, weight, and blood pressure.Source: 1) Dataset · p. 3
  • To increase quantitative evaluation sample size, the authors generated 365 days of synthetic data for 100 participants with individualized treatment effects.The synthetic participants use causal graphs matching the N-of-1 graph, random individual effects, and nutrition and health values sampled from a Gaussian distribution fitted to N-of-1 data.Source: 1) Dataset · pp. 3–4

B. Architecture

ChatDiet’s architecture uses an orchestrator to filter, fuse, and transcribe outputs from personal and population models alongside user queries. Its implementation retrieves query-relevant nutrition effects and foods, then prompts an LLM to produce recommendations and explanations.

B. Architecture

  • ChatDiet has four components: an orchestrator, personal model, population model, and LLM.Source: B. Architecture · p. 4

1) Orchestrator:

  • The orchestrator filters and fuses model outputs with user queries, retrieving only information relevant to the specific inquiry.For extensive health data, it can select only the subset relevant to the requested outcomes.Source: 1) Orchestrator · p. 4
  • The orchestrator transcribes non-textual inputs such as time series into text that the LLM can process.Source: 1) Orchestrator · p. 4
  • Orchestrator prompts instruct the LLM to follow supplied personal nutrition effects and food information, and to explain recommendations step by step.The implementation uses instruction templates and zero-shot chain-of-thought prompting.Source: 1) Orchestrator · pp. 4–5
  • The case-study orchestrator first retrieves relevant nutrition effects, then ranks foods by nutrition content per calorie and selects the top 10.BM25 retrieves query-relevant effects and foods; the foods are selected after ranking.Source: 1) Orchestrator · p. 4
  • Personal nutrition effects are represented as causal effects on health outcomes, alongside food ingredient and nutrition information for the LLM.The example encodes vitamin B1’s estimated effect on deep-sleep duration and ingredient quantities for beet greens.Source: 1) Orchestrator · pp. 4–5

2) Personal Model:

The Personal Model uses individual data and causal discovery and inference to estimate how nutrition relates to a person’s health outcomes. Its data can include food preferences, dietary history, health records, and wearable signals.

2) Personal Model:

  • Personal data can include food preference ratings, dietary history, electronic health records, and wearable physiological signals.The framework can also represent these data in varied formats, including embeddings or textual descriptions.Source: 2) Personal Model · p. 5
  • Causal discovery and inference are used to determine the causal effects of nutrition on health outcomes, going beyond correlation.The case study applies these methods to a personal model.Source: 2) Personal Model · p. 5
  • Structural Agnostic Modeling learns causal generative models adversarially, balancing conditional-independence relations and distributional data asymmetries against model complexity and data fit.Source: 2) Personal Model · p. 5
  • After constructing a causal graph, the framework applies inference methods to estimate causal impacts; the average treatment effect quantifies an intervention’s average impact on an outcome.Source: 2) Personal Model · p. 5
  • When the final causal graph contains a mediator path, mediator analysis is used to estimate effects through the intermediate variable.The analysis distinguishes direct effects from indirect effects mediated through that variable.Source: 2) Personal Model · p. 5
  • The case study uses the Causal Discovery Tool to build a graph from personal data, then DoWhy to estimate effects using that data and graph.Source: 2) Personal Model · p. 5

C. Population Model

The Population Model supplies general food and nutrition knowledge to ground recommendations in broader dietary trends and health norms. In the case study, it loads nutrition information from a food database directly into the Orchestrator.

C. Population Model

  • Population knowledge can include food knowledge graphs, nutritional standards, dietary guidelines, and nutrition or supplement facts.It is used downstream to add generalized knowledge to recommendations.Source: C. Population Model · p. 5
  • In the case study, the Population Model selects food ingredients, nutrition content, and sensory properties from the Cronometer Food Logger database for the Orchestrator.Nutritionix and other API-accessible databases are also identified as possible sources.Source: C. Population Model · pp. 5–6
  • Directly loading food information avoids additional embedding extraction and makes general nutrition knowledge readily accessible to ChatDiet.Source: C. Population Model · p. 6

D. Generative Response

ChatDiet’s Generative Response uses an LLM to turn Orchestrator-curated context into personalized food recommendations. The evaluation reports personal-model findings and a recommendation test, while noting that standardized benchmarks and metrics for comprehensive personalization assessment are unavailable.

D. Generative Response

  • The Generative Response uses the language model to process Orchestrator-curated text, combining personal context, population knowledge, and the user’s query for recommendations.The case study employs gpt-3.5-turbo to process the Orchestrator’s data and generate responses.Source: D. Generative Response · p. 6

IV. CHATDIET EVALUATION

  • The evaluation presents a recommendation test and illustrative demonstrations, but comprehensive personalization assessment awaits standardized benchmarks and metrics.Source: §IV · p. 6

A. Personal Model Results

  • The personal causal graph shows associations between nutrient intake and activity, sleep, and heart-health indicators, including interactions among macronutrients and health metrics.Specific fat types such as omega-3 and omega-6 are linked to heart-rate variability and energy levels.Source: A. Personal Model Results · p. 6
  • Vitamin D, Iron, and Magnesium show significant effects on sleep quality and resting heart rate, while omega-3 and carbohydrate show effects on HRV, sleep efficiency, and activity burn.Source: A. Personal Model Results · p. 6

B. Quantitative Validation of Effectiveness

  • The recommendation test generated 400 responses from 100 synthetic participants across four target outcomes and classified recommendations as correct when explanations matched personal nutrition effects.The recommendation effectiveness ratio is the proportion of recommendations classified as correct; the supplied passages do not give its result.Source: B. Quantitative Validation of… · p. 6

C. ChatDiet’s Explainability Demonstration

ChatDiet explains recommendations through personalized nutrition effects and demonstrates food suggestions linked to a health goal. Its personalization demonstration contrasts general food knowledge with recommendations that incorporate an individual’s nutrition effects.

C. ChatDiet’s Explainability Demonstration

D. ChatDiet’s Personalization Demonstration

  • Personalization means tailoring recommendations to an individual’s nutrition effects rather than relying solely on population knowledge.The demonstration compares recommendations with and without personal data.Source: D. ChatDiet’s Personalization… · p. 7
  • For Salmon, the personalized rationale considers Omega-3’s adverse impact alongside protein’s constructive influence, in contrast to the broader population view of Omega-3.Source: D. ChatDiet’s Personalization… · p. 7
  • The authors state that recommendations based on personal nutrition effects can align dietary choices more closely with users’ physiological requirements.Source: D. ChatDiet’s Personalization… · p. 7

E. ChatDiet’s Interactivity Demonstration

ChatDiet supports interactive follow-ups, including requests for further explanation and changes when users reject a suggested food.

E. ChatDiet’s Interactivity Demonstration

  • ChatDiet handles follow-up questions and can adapt recommendations when users express changed preferences.Users can seek insights, adjustments, or express preferences; when a user dislikes the suggested food, ChatDiet offers alternatives.Source: E. ChatDiet’s Interactivity… · pp. 7–8
  • Users can request clarification when an initial response lacks a detailed explanation.Figure 5 illustrates ChatDiet responding to a follow-up question asking for further explanation.Source: E. ChatDiet’s Interactivity… · p. 7, Figure 5 · p. 8
  • When a user is uninterested in the suggested food, ChatDiet promptly offers alternative options.Figure 6 depicts a follow-up about altered preferences.Source: E. ChatDiet’s Interactivity… · p. 8

V. DISCUSSION

ChatDiet’s examples demonstrate explainability, personalization, and interactive adaptation, while the authors identify limits tied to available data and occasional contradictory recommendations.

V. DISCUSSION

  • ChatDiet uses personalized nutrition effects to present dietary suggestions in the context of health outcomes, supporting a transparent decision process.The discussion describes recommendations customized to an individual’s unique nutrition effects and physiological profile, beyond population nutrition knowledge.Source: §V · p. 8
  • ChatDiet responds to follow-up questions and evolving preferences, allowing users to seek insights and make real-time adjustments.The authors describe this as supporting user control and dynamic conversations.Source: §V · p. 8
  • Suggestions are limited to factors available in the dataset, which comes from one subject and covers specific health outcomes and smart devices.Queries that omit explicit factor names can also lead to occasional inaccuracies and nonsensical recommendations.Source: §V · p. 8
  • Some recommendations contradict the displayed personal nutrition effects, including an inaccurate almond recommendation linked to Tryptophan and improved sleep.The authors say such self-contradiction undermines the credibility and reliability of recommendations.Source: §V · p. 8

VI. CONCLUSION

  • 92% effectiveness was reported for ChatDiet’s food recommendations.The authors also describe a chatbot implemented using three years of one individual’s dietary habits, health metrics, and related information.Source: §VI · p. 9

VIII. APPENDIX

The appendix identifies a personal nutrition-effect causal graph estimated from N-of-1 data.

VIII. APPENDIX

  • The graph represents causal relationships between nutrition and health outcomes.The text describes it as an estimated graph.Source: §VIII · p. 10, Figure 8 · p. 11
  • The estimated graph is derived from N-of-1 data.Figure 8 is identified as the Personal Model’s graph.Source: §VIII · p. 10, Figure 8 · p. 11
  • Figure 8 is the personal nutrition-effect causal graph from the Personal Model.The accompanying text identifies it as an estimated causal graph between nutrition and health outcomes.Source: §VIII · p. 10, Figure 8 · p. 11
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