Ingredient Inspired Recipe Recommender: Your Culinary Guide

Abstract

Abstract: This research explores the development of an innovative culinary solution – the ingredient-Inspired Recipe Recommender. Focused on addressing the global challenge of food wastage, exacerbated by both consumer habits and quality deterioration, our study employs advanced deep learning architectures. A key aspect involves a detailed comparison of ResNet models (ResNet-50, ResNet-101, and ResNet-152) alongside alternative architectures like DenseNet, VGG, and xResNet. The objective is to identify the most effective neural network for accurately recognizing ingredients and generating insightful recipe recommendations. In response to the contemporary issues of rushed lifestyles, processed food reliance, and insufficient attention to nutrition, our research aims to empower individuals with a practical, technology-driven tool. By seamlessly integrating deep learning into the culinary landscape, the ingredient-Inspired Recipe Recommender suggests personalized recipes based on available ingredients, fostering healthier eating habits and contributing to a reduction in food wastage. This paper presents the methodology employed for the comparative analysis, reports experimental results, and discusses broader implications within the realms of food sustainability and technological innovation. In addressing the evolving needs of individuals, our research strives to align technology and gastronomy for positive environmental and societal impact.