Web-based Cooking Recipe Recommender System based on Stocked Groceries

Abstract

Due to the severity of the recent PM 2.5 and COVID-19 pandemic situations, the work-from-home lifestyle has been widely adopted as a new normal. Consequently, it’s necessarily preferable to cook at home instead of dining out as usual. However, a common problem is that there are unplanned and overstocked grocery items which are usually unrecognized and improperly managed. To address this issue, the "What To Cook" web application was developed with the theoretical application of Term Frequency-Inverted Document Frequency (TF-IDF) calculations to search for recipes based on the stocked groceries and then applied Cosine Similarity to calculate the similarity between each recipe and the stocked grocery items. Users can input the list of own stocked grocery items into the application and then apply the content-based filtering system to recommend recipes that can utilize the stocked grocery items. Additionally, the application supports the image capturing using Google Cloud Vision API. The application also stores the user's cooking history and saves the under interested recipes for future reference. After testing the application in real-world scenarios, it was found to be easy to use with satisfiable results.