[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123942-en":3,"doc-seo-123942-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123942,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Helping the Home Cook - How Unsupervised Machine Learning Can Prevent Food Waste - Honors Project 2024","A common home-cooking challenge is having too much of specific ingredients left over, with no clear way to use them before they expire. This work proposes an unsupervised machine learning approach that recommends recipes based on the ingredients the home cook wants to use. The method uses FastText and trains on the RecipeNLG dataset, then selects recipes by comparing ingredient sets through similarity. By enabling better recipe matching for spare pantry items, the solution aims to reduce consumer food waste while supporting practical, ingredient-driven cooking decisions.","Seattle Pacific University  \nDigital Commons @ SPU  \n\n| Honors Projects | University Scholars |\n| --- | --- |\n| Spring 5-18-2024\u003Cbr>Helping the Home Cook: How Unsupervised Machine Learning Can Prevent Food Waste\u003Cbr>Ryan B. Watson\u003Cbr>Seattle Pacific University\u003Cbr>Follow this and additional works at: [https://digitalcommons.spu.edu/honorsprojects](https://digitalcommons.spu.edu/honorsprojects)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons |  |\n\nRecommended Citation  \nWatson, Ryan B., \"Helping the Home Cook: How Unsupervised Machine Learning Can Prevent Food Waste\" (2024) . Honors Projects. 216.  \n[https://digitalcommons.spu.edu/honorsprojects/216](https://digitalcommons.spu.edu/honorsprojects/216)  \nThis Honors Project is brought to you for free and open access by the University Scholars at Digital Commons @ SPU. It has been accepted for inclusion in Honors Projects by an authorized administrator of Digital Commons @ SPU.  \nHelping the Home Cook: How Unsupervised Machine Learning Can Prevent Food Waste  \nby  \nRyan Watson  \nFACULTY MENTOR:  \nDR. DENNIS VICKERS  \nHONORS PROGRAM DIRECTOR:  \nDR. JOSHUA TOM  \nA project submitted in partial fulfillment of the requirements  \nfor the Bachelor of Arts degree in Honors Liberal Arts  \nSeattle Pacific University  \n2024  \nPresented at the SPU Honors Research Symposium Date May 18th, 2024  \nHelping the Home Cook: How Unsupervised Machine Learning Can Prevent Food Waste  \nRyan Watson  \nSeattle Pacific University, Department of Computer Science  \nSeattle, Washington  \n[watsonr3@spu.edu](watsonr3@spu.edu)  \nAbstract -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.  \nKeywords - NLP, Unsupervised Machine Learning, FastText, Genism, NLTK, Word Embeddings, RecipeNLG, Information Retrieval  \nI. INTRODUCTION  \nHome cooks around the world tend to buy the amount of food they think they will need, not the amount they actually need. This lack of divine foresight often results in a wide variety of leftover food ingredients. It is difficult for a home cook to figure out a recipe that uses these assorted ingredients because the variety of spare ingredients often were components of unrelated recipes. This can lead to food waste because the home cook may not be able to think of a recipe that uses the spare ingredients before they expire. Dou et al. estimated that consumers in the United States waste over 40 million metric tonnes of edible food each year [1] . On top of this, nearly half of all edible food waste is created by consumers [1] . An unsupervised machine learning model can help the home cook find recipes that they can make with the ingredients that they have. By providing recipe recommendations for the ingredients that the user has, food waste can be reduced, and home cooks can feel empowered to utilize their leftover food instead of letting it expire or throwing it away.  \nNatural Language Processing (NLP) is a field of study that combines the powers of linguistics with machine learning to recognize, modify, and generate text [2] . Machine learning applications require a dataset, ideally one with a large quantity of high-quality data, to train the model on. RecipeNLG is a high-quality dataset that contains information about 2.6 million recipes [3] . I claim that NLP techniques can be used with RecipeNLG to effectively recommend recipes based on a list of ingredients.  ","cbCaivaM69HG9vor","https://ap.wps.com/l/cbCaivaM69HG9vor","pdf",1118352,1,10,"English","en",105,"# Introduction\n## Problem of leftover ingredients and food waste\n## NLP and the RecipeNLG dataset\n# Related Work\n## Recipe generation approaches\n## Recipe quality improvement via ML\n## Information retrieval and similarity-based recommendation","[{\"question\":\"What problem does the project address for home cooks?\",\"answer\":\"Home cooks often buy more of certain ingredients than they need, leaving leftovers that are hard to incorporate into suitable recipes before they expire.\"},{\"question\":\"How does the proposed model recommend recipes?\",\"answer\":\"It uses an unsupervised machine learning model built with FastText, trained on RecipeNLG, and recommends recipes by selecting the ingredient set most similar to the user-provided ingredients.\"},{\"question\":\"Why is information retrieval emphasized instead of generating new recipes?\",\"answer\":\"The project argues that finding an existing recipe that best matches the user’s desired ingredients is a better fit than generating parts or whole recipes from scratch.\"}]","Helping the Home Cook - 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