Date of Award
6-5-2025
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2025
Departments
Computer Science and Engineering; General Engineering
First Advisor
Jessica Kuczenski
Second Advisor
Ahmed Amer
Abstract
Individuals with dietary restrictions or specific health goals often struggle to find recipes that meet their needs. This challenge is further compounded by the lack of time and convenience many busy individuals face when planning and preparing meals. The BIOME app addresses this issue by o↵ering a mobile application that delivers personalized recipe recommendations based on user preferences, dietary restrictions, and ingredient availability. It also allows users to order ingredients directly through Instacart.
BIOME features a user-friendly frontend built with Flutter and a backend powered by Google Firebase, integrated with unsupervised machine learning to provide tailored content. A clustering algorithm was used to group similar recipes based on ingredients and nutritional information. The model’s e↵ectiveness was evaluated using metrics such as the silhouette coefficient and Davies-Bouldin index to ensure distinct and meaningful groupings of recipes. BIOME connects to the Instacart API to enhance usability, allowing users to convert ingredient lists into shopping links for faster, easier grocery access. Internal testing with mock users and a beta testing phase with friends and family helped identify bugs and gather feedback to improve performance and user experience.
Overall, BIOME demonstrates how machine learning and mobile design can support healthier eating habits by offering better recipe recommendations and reducing the time and effort needed to cook nutritious meals.
Recommended Citation
McCaulley, Ryan and Chen, Jerry, "BIOME to Transform Cooking" (2025). Computer Science and Engineering Senior Theses. 314.
https://scholarcommons.scu.edu/cseng_senior/314
