Personal Food Model

TL;DR

This paper introduces the Personal Food Model, a comprehensive digital representation of an individual's food preferences and biological responses, integrating multimedia data and a novel 6-dimensional taste-space to enhance personalized food recommendations.

Contribution

It presents a person-centric, multimodal framework combining multimedia, biological data, and a new taste-space for personalized food modeling and recommendation systems.

Findings

Development of a 6-dimensional taste-space for culinary characterization

Integration of multimedia and biological data for personalized food modeling

Use of event mining to relate food to biological and life events

Abstract

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.