Culinary Curator: An AI-Powered Recipe Assistant

In today’s data-driven world, the intersection of artificial intelligence, natural language processing(NLP) and Computer Vision(CV) has given rise to innovative solutions in various domains. One intriguing application area is recipe generation, thus this research proposes the Culinary Curator, a novel culinary assistant that utilizes AI, NLP and CV to simplify meal planning and promote healthier eating choices. This study presents a comparative analysis of five distinct models for recipe recommendation and two models for ingredient recognition. Through rigorous evaluation and performance metrics, this study offers insights into the effectiveness of these models. These findings contribute to the selection of the best AI models for promoting optimal utilization of ingredients available at user’s hand, thereby reducing wastage as well as enhancing culinary experiences. This research informs the development of AI-powered recipe recommendation systems and underscores the significance of model choice in achieving accuracy and efficiency.

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Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/icisaa62385.2024.10828847
Citations
2
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.69
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article

Culinary Curator: An AI-Powered Recipe Assistant

Ishani Saha, Alireza Lari, Vedant Naik, Khushi Tejwani
2 citations
AI in Service Interactions
0.69
article

Culinary Curator: An AI-Powered Recipe Assistant

Ishani Saha, Alireza Lari, Vedant Naik, Khushi Tejwani
article en
2 citations

Abstract

In today’s data-driven world, the intersection of artificial intelligence, natural language processing(NLP) and Computer Vision(CV) has given rise to innovative solutions in various domains. One intriguing application area is recipe generation, thus this research proposes the Culinary Curator, a novel culinary assistant that utilizes AI, NLP and CV to simplify meal planning and promote healthier eating choices. This study presents a comparative analysis of five distinct models for recipe recommendation and two models for ingredient recognition. Through rigorous evaluation and performance metrics, this study offers insights into the effectiveness of these models. These findings contribute to the selection of the best AI models for promoting optimal utilization of ingredients available at user’s hand, thereby reducing wastage as well as enhancing culinary experiences. This research informs the development of AI-powered recipe recommendation systems and underscores the significance of model choice in achieving accuracy and efficiency.

Narsee Monjee Institute of Management Studies (IN)
Zero hunger
Openalex Percentile: Top 22%
AI in Service Interactions
0.69
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