MasterCook Recipe Recommendation System

Abstract

"In the history of information that has been forgotten, recipe recommendation systems play a major role in providing individuals with meal planning and cuisine exploration. This report presents the development of a content-based recipe recommendation system using NLP techniques. The author's research leverages a diverse recipe dataset, provides text preprocessing, and utilizes TF-IDF-based feature extraction to create recipe and user profiles. The report dives more deeply into data preprocessing, model development, and evaluationusing precision, recall, and F1 score metrics. Additionally, the author discusses theincorporation of flavors and expertise for people who are struggling to find good and easy recipes to make. The author addresses challenges and limitations and proposes directions for future enhancement. This project contributes to the advancement of recipe recommendations, empowering users to explore and enjoy a wide range of cuisine experiences."