Date of Award
Spring 5-18-2024
Document Type
Honors Project
University Scholars Director
Dr. Joshua Tom
First Advisor/Committee Member
Dr. Dennis Vickers
Keywords
NLP, Unsupervised Machine Learning, FastText, Information Retrieval, Word Embeddings, RecipeNLG
Abstract
A common problem for the home cook is having too much of one food ingredient leftover, then not knowing what to do with it. To alleviate this problem, I propose using an unsupervised machine learning model to recommend recipes based on what ingredients the home cook wants to use. This model is built with FastText and trained on the recipe ingredients in the RecipeNLG dataset. Recipes are recommended based on which recipe ingredient set is most similar to the recipe ingredients provided in the user input. This solution will reduce consumer food waste by giving the home cook the information required to utilize the spare ingredients they have lying around their kitchen.
Recommended Citation
Watson, Ryan B., "Helping the Home Cook: How Unsupervised Machine Learning Can Prevent Food Waste" (2024). Honors Projects. 216.
https://digitalcommons.spu.edu/honorsprojects/216
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