IMVB7t: A Multi-Modal Model for Food Preferences based on Artificially Produced Traits

TL;DR

This study introduces IMVB7t, a multi-modal AI model that predicts food preferences based on environmental visual stimuli by extracting key attributes and employing ensemble and decision tree methods.

Contribution

The paper presents a novel multi-modal model, IMVB7t, combining multiple attribute detection and decision trees to analyze visual influences on food choices.

Findings

Ensemble model achieved 0.85 detection accuracy.

Decision tree-based recommendations reached 0.96 accuracy.

Study establishes a foundation for interdisciplinary research on visual stimuli and food preferences.

Abstract

Human behavior and interactions are profoundly influenced by visual stimuli present in their surroundings. This influence extends to various aspects of life, notably food consumption and selection. In our study, we employed various models to extract different attributes from the environmental images. Specifically, we identify five key attributes and employ an ensemble model IMVB7 based on five distinct models for some of their detection resulted 0.85 mark. In addition, we conducted surveys to discern patterns in food preferences in response to visual stimuli. Leveraging the insights gleaned from these surveys, we formulate recommendations using decision tree for dishes based on the amalgamation of identified attributes resulted IMVB7t 0.96 mark. This study serves as a foundational step, paving the way for further exploration of this interdisciplinary domain.