# Personal Food Model

arXiv: https://arxiv.org/abs/2008.12855 · HTML: https://mlsanity.com/paper/2008.12855/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework](https://mlsanity.com/paper/2403.00781/) (2024)
- [Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity](https://mlsanity.com/paper/2103.14536/) (2021)
- [Food Recommendation as Language Processing (F-RLP): A Personalized and Contextual Paradigm](https://mlsanity.com/paper/2402.07477/) (2024)
- [World Food Atlas Project](https://mlsanity.com/paper/2504.18727/) (2025)
- [Towards Integrative Multi-Modal Personal Health Navigation Systems: Framework and Application](https://mlsanity.com/paper/2111.10403/) (2021)
- [Meta-Task: A Method-Agnostic Framework for Learning to Regularize in Few-Shot Learning](https://mlsanity.com/paper/2402.18599/) (2024)

## Abstract (arXiv)

Food is central to life. Food provides us with energy and foundational building blocks for our body and is also a major source of joy and new experiences. A significant part of the overall economy is related to food. Food science, distribution, processing, and consumption have been addressed by different communities using silos of computational approaches. In this paper, we adopt a person-centric multimedia and multimodal perspective on food computing and show how multimedia and food computing are synergistic and complementary. Enjoying food is a truly multimedia experience involving sight, taste, smell, and even sound, that can be captured using a multimedia food logger. The biological response to food can be captured using multimodal data streams using available wearable devices. Central to this approach is the Personal Food Model. Personal Food Model is the digitized representation of the food-related characteristics of an individual. It is designed to be used in food recommendation systems to provide eating-related recommendations that improve the user's quality of life. To model the food-related characteristics of each person, it is essential to capture their food-related enjoyment using a Preferential Personal Food Model and their biological response to food using their Biological Personal Food Model. Inspired by the power of 3-dimensional color models for visual processing, we introduce a 6-dimensional taste-space for capturing culinary characteristics as well as personal preferences. We use event mining approaches to relate food with other life and biological events to build a predictive model that could also be used effectively in emerging food recommendation systems.